thesis Yuhan Wang (1)

Market Basket Analysis of Ingredients and Flavor Products

by Yuhan Wang

A THESIS

submitted to

Oregon State University

Honors College

in partial fulfillment of the requirements for the

degree of

Honors Baccalaureate of Science in Business Information System (Honors Associate)

Presented August 31, 2016 Commencement June 2017

2

3

AN ABSTRACT OF THE THESIS OF

Yuhan Wang for the degree of Honors Baccalaureate of Science in Business Information System presented on August 31, 2016. Title: Market Basket Analysis of Ingredients and Flavor Products

Abstract approved:_____________________________________________________

Bin Zhu

In today, information plays a more and more important role in business world.

The companies are full of data, but poor of valuable information extracted from that

diverse data. Data mining, as a process of transferring data from variety perspectives

to useful information, is the new trend for businesses. It can be used to make more

targeted business model or strategies decisions.

Market basket analysis is one of the most useful modeling techniques in data

mining. It usually used to analyze customer purchases behaviors. The results can

provide decision-makers more valuable information such as marketing strategies

making, inventory controlling, and cross sales.

The main objective of this thesis is to study how 15 flavor products sold by

Zengcheng Handyware Seasoning Company in five different regions interrelate. And

based on the correlation results, how to provide valuable information to improve

company’s marketing activities.

Key Words: data mining, market basket analysis, association rules Corresponding e-mail address: [email protected]

4

©Copyright by Yuhan Wang August 31, 2016

All Rights Reserved

5

Table of Content Chapter 1: Introduction ............................................................................................. 7

1.1 Overview ..................................................................................................................................... 7

1.2 Research Problem Description ................................................................................................. 7

Chapter 2: Data ........................................................................................................... 9

2.1 Data Description .................................................................................................................... 9

2.1.1 Basic description ............................................................................................................... 9

2.1.2 Description of regions ....................................................................................................... 9

2.1.3 Description of dataset ..................................................................................................... 11

2.2 Dataset Adjustment ............................................................................................................. 12

2.3 Considerations and Assumptions ....................................................................................... 13

2.4 Research Questions ............................................................................................................. 14

Chapter 3: Research Methodology .......................................................................... 15

3.1 Market Basket Analysis .......................................................................................................... 15

3.2 Association Rule ...................................................................................................................... 15

3.2.1 Definition of Association Rule......................................................................................... 15

3.2.2 Process of Association Rule ............................................................................................. 18

3.2.3 Frequent Set of Items Generation .................................................................................. 18

3.2.4 Association Rules Generation ......................................................................................... 22

Chapter 4: Data analysis and results ...................................................................... 24

4.1 Market Basket Analysis .......................................................................................................... 24

4.1.1 Analysis Package Selection -- SPSS ................................................................................ 24

4.1.2 SPSS Modeling Process ................................................................................................... 25

4.2 Results .......................................................................................................................................... 28

4.2.1 Result for All Regions ...................................................................................................... 28

4.2.2 Result for the North China Region ................................................................................. 32

4.2.3 Result for the South China Region ................................................................................. 36

6

4.2.4 Result for the Mid China Region .................................................................................... 39

4.2.5 Result for the East China Region ................................................................................... 43

4.2.6 Result for the South West China Region ....................................................................... 46

Chapter 5: Conclusions ............................................................................................ 50

5.1 General Discussion .................................................................................................................. 50

5.2 Academic Contribution .......................................................................................................... 50

5.3 Business Contribution ............................................................................................................ 51

5.4 Limitations of the study .......................................................................................................... 51

5.5 Directions for Future Research ............................................................................................. 52

Bibliography .............................................................................................................. 53

Appendices ................................................................................................................. 54

Appendix A: Data Description ..................................................................................................... 54

Appendix B: Results ..................................................................................................................... 58

7

Chapter 1: Introduction 1.1 Overview

Today, we live in the age of information. More and more data from multiply

dimensions are surrounding us. For the companies, the age of information provide

them an opportunity to collect enormous amounts of data. In order to transfer the data

into information, data mining become more and more popular in business world.

Data mining usually starts from data collection. And clients are the source of

data collection. Every purchasing transaction includes multi-angles data about clients.

By analyzing and summarizing the large number of data, it is easy to extract useful

information from raw data. The hidden patterns or models can be found also. The

valuable information includes hidden patterns can be used in multiply business

activities such as increase profit, decrease cost, and making market strategies.

There are various statistical algorithms in data mining. For instance,

classification, clustering, regression, artificial intelligence, neural networks, decision

trees, and association rules are all important data mining techniques to study

knowledge from data (Ramageri). In this study, we decided to use association rules,

which also called market basket analysis, to mining the provided dataset.

As a data mining technique, market basket analysis always focuses on “what

goes with what”. It can provide the researcher more information than just the products

in the “shopping cart”. In this research, market basket analysis method can help us to

study the relationship between different flavor products purchases in different regions.

Then, the deeper analysis results can provide the company more useful marketing

strategy recommendations and guides for any specific region.

1.2 Research Problem Description In the past decades, market basket analysis usually appealed in retail market

or e-commerce market. However, the advanced technology makes not only retailers

have the opportunity to collect customers’ data, manufacturer can also gather their

clients’ information to provide high quality products and technical support.

Companies in multiple industries can use market basket analysis to help their

decision-making. This technique can help the business to eliminate the blind market

8

and know more about their customers. By applying market basket analysis, we can

find valuable information about customers, and hidden connection between products

that help decision-makers to do further strategy making.

The main objective of this thesis is finding out how to create or improve

recommendation of flavor products to customers in different regions based on their

shopping behaviors. Mining association rules based on analyzing transaction-based

dataset can provide us useful information about co-purchases products. Classified the

dataset by regions will help us to figure out the product preferences in different

regions.

9

Chapter 2: Data 2.1 Data Description

2.1.1 Basic description The dataset used in this study is a collection of 15 different flavor products’

sales records from 2013 to 2015. The study is based on this simulated dataset

provided by Zengcheng Handyware Seasoning Company. The simulated dataset still

follows the original dataset’s trend and characteristics. The sales records were

classified by both year and region. In the dataset, there are five main regions: the

South China Region, the North China Region, the Mid China Region, the East China

Region, and the South West China Region.

2.1.2 Description of regions There is an obvious distance between the South China Region and the other

four regions. And the sales volume for the South West China Region is only half of

the South China Region. See Table 2.1 for more detailed information.

Table 2.1 Regional Monthly Sales (kg) Comparison

South

China

North

China

Mid

China

East

China

South West

China

Jan. 43270.00 26500.33 25019.33 30633.67 23089.67

Feb. 20637.67 10923.00 13426.33 13542.00 11363.00

Mar. 38186.67 24626.67 23568.00 27279.33 21145.00

Apr. 35506.67 24187.33 22812.00 25886.67 19758.33

May. 38698.33 22212.67 20531.67 27705.67 15933.67

Jun. 32966.33 22851.00 20334.67 26991.33 19392.33

Jul. 47471.33 29238.33 24165.67 29502.00 20293.67

Aug. 55559.00 29363.67 29857.33 32669.67 23031.33

Sep. 62206.33 34186.00 29722.00 38224.00 29586.00

Oct. 47238.67 22657.33 22453.33 29942.67 16073.67

Nov. 59163.00 27041.00 25675.67 28846.67 19397.33

Dec. 56090.67 33801.67 28716.00 30300.33 23652.67

10

The average monthly sales volume for different regions also showed us the

aggressiveness of the South China Region. This region’s monthly average sales

volume is the double of the South West Region, which is the weakest during the past

three years. See Table 2.2 for more detailed statistics.

Table 2.2 Regional Sales Comparison

Average Sales

per month

(kg)

Average

Monthly Sales

in 2013 (kg)

Average

Monthly Sales

in 2014 (kg)

Average

Monthly Sales

in 2015 (kg)

South 44,749.56 39,591.50 42,350.17 52,307.00

North 25,632.42 24,102.17 23,889.67 28,905.42

Mid 23,856.83 20,150.50 22,078.08 29,341.92

East 28,460.33 28,556.83 29,613.67 27,210.50

South West 20,226.39 18,443.00 19,870.00 22,366.17

The fluctuations of the company’s total monthly sales from 2013 to 2015 are

stable. The sales volume always starts from the lowest February then increase to the

first high value at March. After fluctuating smoothly from March to June, it increases

to the peak on September substantially. From September to next February, the sales

volume usually fluctuates smoothly. In 2013 to 2015, peaks are located at the end of

the summer (August or September), and the lowest point happens in February.

The reason why both of February and October have lower monthly sale is

related to nationwide holiday. In February, there is a 15 days break nationwide in

China to celebrate spring festival. And in October, there is a 7 days nationality

holiday. During the holiday, there are no orders and productions. Therefore, both of

these two months usually have a lower sales than other months.

The peak happened in August related to summer break schedule. In China, all

of the primary schools, middle schools, high schools, and universities have the

summer break during July to September. As the main purchasing power, students will

consume more snacks products during the summer break. So during August, the sales

usually goes well. Another peak in November represents that manufacturers are all

preparing for the spring festival. Every year, the snacks consumption during spring

11

festival is very high. Therefore, November usually is another high point during the

year. Figure 2.1 Regional Monthly Sales

2.1.3 Description of dataset The raw dataset is classified by region. All five regions’ sales records have

been listed. The transaction database consists of the following five elements:

• Product name – name of the products sold in all regions. Abbreviation

descriptions are listed. (Table 1, appendix A)

• Month – month of information collected

• Year – year of information collected

• Region – the name of the five different regions

• Sales volume – the sales volume in kilogram

This dataset includes 15 products, which sold in five regions. Some products,

such as Tomato Flavor and Crab Flavor, have the greater volume of sales than other

flavors since they have a wider market acceptance. Most end products includes these

flavors. Some products’ sales, such as Chives flavor or Hot & Spice Sichuan Flavor

are much lower than the average monthly sales because of the small client base.

These kind of products are currently not mainstream product in the market. They are

only accepted by a small group of customers. Hence, there is a big sales gap between

popular products and niche products. For instance, the most popular product, Tomato

Flavor, has a ten times average monthly sales than the Hot & Spice Sichuan Flavor.

0

20000

40000

60000

80000

Jan. Feb. Mar. Apr. May. Jun. Jul. Aug. Sep. Oct. Nov. Dec.

KILO

GRAM

2013 -‐ 2015 Regional Monthly Sales

2013 2014 2015

12

And most products’ average monthly sales are lower than the average number. Table

2.3 provides more detailed information.

Table 2.3

Product Name Average number of

unique items per

month

Difference with

Total Average Sales

Tomato Flavor 8462.61 6556.93

Crab Flavor 5406.44 3500.77

Sauced Beef Flavor 1726.86 -178.81

Cheese Corn Flavor 1632.90 -272.77

Barbecue Flavor 1617.11 -288.56

Pepper Beef Steak Flavor 1395.00 -510.67

Chicken Flavor 1203.71 -701.96

Curry Barbecue Flavor 1168.66 -737.01

Cumin Barbecue Flavor 1160.65 -745.02

Hot & Spicy Salt Flavor 959.89 -945.78

Pork Steak Flavor 920.27 -985.41

Yolk Flavor 902.23 -1003.44

Kimchi Flavor 541.23 -1364.45

Chives Flavor 592.36 -1313.31

Hot & Spicy Sichuan Flavor 895.18 -1010.50

2.2 Dataset Adjustment One of the crucial points of the Market Basket Analysis is studying how a

product cross sell to customers. In another words, researcher usually more focused on

what kind of product that customers purchased instead of how many they purchased.

Therefore, depending on the research questions, the raw dataset should be adjusted

from the original sales-based to a true or false dataset.

A true or false dataset should only include the information about whether do

customers purchase the products or not. In usual, “1” is used to represent true, which

the customer purchases this specific product, and “0” means false, which the

13

customer does not purchase it. In order to adjust the dataset, we decided to find a cut-

off point to differentiate the data between “1” and “0”.

In the given dataset, the certain differences in products’ sales volume were too

large to be ignored. We cannot use one fix number as the cut-off point for all products.

What’s more, since the provided dataset is simulate, there may also has inevitable

bias during the simulation process.

In order to avoid the bias and decrease the simulation error, we decided to use

the mean as the cut-off point. For the dataset includes all sales records in five regions,

the cut-off point for every product is the average monthly sales volume in all regions

for each product. For the dataset only includes specified region, the cut-off point for

every product is the average monthly sales volume in specified region. If the monthly

sales is higher than the cut-off point, it was marked as “1”, if lower, then “0”.

The adjusted datasets are available (see Appendix A, Table 2).

2.3 Considerations and Assumptions 1. Sales Record

The given dataset represents the 15 products’ monthly sales in five regions

from 2013 to 2015. However, since the Market Basket Analysis requires the

transaction-based dataset, we decide to treat one region’s one-month sales record

as one transaction.

2. Language

The initial dataset is in Mandarin. So the names of all products have been

translated to English (See Appendix A, Table 1).

3. Simulation

The given dataset is not the real sales records. All of the data have been

simulated for confidentiality purpose. But the simulated datasets keep the main

original trend and characteristics. Bias may exist because of the uncertainty of

simulation.

4. Correlation

A general assumption in this study is that sales of different products are

correlated.

14

2.4 Research Questions Regional-oriented Research Questions: 1. What kinds of product are frequently purchased together in all five regions?

2. What kinds of product are frequently purchased together in South China Region?

3. What kinds of product are frequently purchased together in North China Region?

4. What kinds of product are frequently purchased together in Mid China Region?

5. What kinds of product are frequently purchased together in East China Region?

6. What kinds of product are frequently purchased together in South West China

Region?

15 products sold in all five regions will be analyzed for finding co-purchases

in different regions. This regional-oriented analysis will provide the company with

valuable information in further marketing strategies setting.

The Association Rule Analysis as the outcome of the Market Basket Analysis

is used to finding the frequent purchasing pattern, correlation and associations based

on the given dataset (Tan, Steinbach and Kumar). It can help us to mine the given

dataset and do recommendations.

15

Chapter 3: Research Methodology

3.1 Market Basket Analysis Based on the given dataset, market basket analysis (MBA) is a perfect method

for this study. The raw data provided by Zengcheng Handyware Seasoning Company

does not have a good pointing. As an undirected data mining technique, market

basket analysis is a good start for knowing the large-scale of dataset (Padoe).

What’s more, there are three levels of market basket data: Customers, Orders,

and Items (Padoe). After observing the dataset, we know this dataset is transaction-

based and items-oriented. It includes five main customers (regions), 180 orders

(transactions), and 15 items (flavor products). All requirements for market basket

analysis are satisfied.

However, the main problem of this study is determining the right cut-off point

during the data adjustment. Using each product’s sales mean as the cut-off point may

generate the error. The uncertain error may affect results. Hence, the possible solution

is using different cut-off points to get the results. Then comparing the results to see

whether the error affect the final conclusions.

3.2 Association Rule As the outcome of the market basket analysis, association rule is a useful data

mining method for mining “frequent patterns, associations, correlations, or causal

structures among sets of items in transaction databases” (Han and Kamber). The

main idea of this technique is producing rules on associations between products from

a transaction-based dataset.

3.2.1 Definition of Association Rule According to the textbooks, Data Mining: Concepts and Techniques written

by Jiawei Han and KAmber Micheline, and Introduction to Data Mining written by

Pangning Tan, Steinbach Michael and Kumar Vipin, We can define the association

rule by the following steps.

Let I = {i1, i2, … , im} be a collection of m items in the market basket data.

Let T= {t1, t2, … , tn} be the set of all transactions in the market basket data.

16

Each transaction ti contains a subset of items from I.

A transaction ti is said to contain an X, which is a collection of items, if X is a

subset of ti (X ⊆ ti , ti ∈ T).

An association rule is an implication expression of the form X à Y.

X and Y are disjoint collection of items (X ≠ ∅, Y ≠ ∅, X ∩ Y = ∅).

The main idea of association rule is finding the relationship between

purchases of different products. It can be represented as:

IF {purchase A & B (A, B ∈ X)} THEN {purchase C (C ∈ Y)}

X is an antecedent. Y is a Consequent.

So we can get:

Antecedent à Consequent [support, confidence]

The strength of an association rule is measured by support and confidence.

Support is the percentage of transactions that include both the antecedent and

the consequent. It determines how often a rule is applicable to the given dataset. If P

means probability, that

Support (X à Y) = P (X � Y)

Table 3.1 Example of Support

ID Items Support Calculus

1 A, B, C Total Support = 5

{AB}: 2; Support {AB} = 2/5 = 40%

{AC}: 2; Support {AC} = 2/5 = 40%

{BC}: 3; Support {BC} = 3/5 = 60%

{ABC}: 1; Support {ABC} = 1/5 = 20%

2 A, B, D

3 A, C

4 B, C

5 B, C, D

Confidence is the percentage of antecedent transactions that also have the

consequent item collection. It determines how frequently items in consequent (Y)

appear in the transactions, which contain the antecedent (X). If P means probability,

that

Confidence (X à Y) = P (Y | X)

17

Table 3.2 Example of Confidence

ID Items Confidence Calculus

1 A, B, C

Confidence {AàB} = {AB}/{A} = 2/3 = 66%

Confidence {BàC} = {BC}/{B} = 3/4 = 75%

Confidence {CàD} = {CD}/{C} = 1/4 = 25%

Confidence {ABàC} = {ABC}/{AB} = 1/2=50%

2 A, B, D

3 A, C

4 B, C

5 B, C, D

Both of the support and confidence are very important in association rule. As

the measures of interestingness, “they respectively reflect the usefulness and certainty

of discovered rules” (Han and Kamber). A low support percentage means there is a

low probability the chosen items were purchased X and Y together. And a low

confidence percentage means there is a low percentage of customers who purchased

X will also bought Y. Therefore, both a minimum support threshold and a minimum

confidence threshold are necessary for this study. We want to find all rules XàY that

satisfied the following two criteria:

• The percentage of X and Y both appear must equal or higher than the

percentage of minimum support threshold of all given transactions.

• The percentage of Y appears in the given transaction that contain X must

equal or higher than the percentage of minimum condition threshold.

The performance of an association rule is measured by lift.

Lift is one of the correlation measures. The occurrence of the set of items, X,

is independent of the occurrence of Y if P (X � Y) = P (X) P (Y); otherwise, X and Y

are dependent and correlated. That is

𝐋𝐢𝐟𝐭 𝐗,𝐘 =𝐂𝐨𝐧𝐟𝐢𝐝𝐞𝐧𝐜𝐞 (𝐀 𝐁)

𝐒𝐮𝐩𝐩𝐨𝐫𝐭(𝐁) =𝐏 (𝐗 ∪ 𝐘)𝐏 𝐗 𝐏(𝐘)

If the value of lift is greater than 1, it indicates a rule that is useful in finding

consequent set of items. In another words, the occurrence of X and Y are positively

correlated. If the value of lift is less than 1, it means X is negatively correlated with Y.

18

If the value of life is equal to 1, it means no correlation between X and Y. Computing

the lift is more useful than only selecting transactions randomly.

3.2.2 Process of Association Rule In general, there is a two-step process to solve the association rule problem

(Han and Kamber).

1. Generating Frequent Set of Items

Setting a minimum support threshold based on the given dataset. Then

generating all sets of items from the given dataset that satisfied the support

exceeds the minimum support threshold.

2. Generating Association Rules

Setting a minimum condition threshold based on the given dataset. Then

generating all sets of items from the frequent set of items that satisfied the

condition exceeds the minimum condition threshold.

3.2.3 Frequent Set of Items Generation This first step in association rule generation is finding all sets of items that

have high support (equal or higher than the minimum support threshold). A lattice

structure usually used to list all possible sets of items. Figure 3.1 shows us the

structure of 5 items (I = {a, b, c, d, e}). Therefore, if a given dataset includes 5 items,

there are 25 possible candidate sets of items and 1 null set of item. Therefore, for a 5

items dataset, there are 25 -1 frequent sets of items.

19

Figure 3.1 Itemset Lattice for Five Products (Tan, Steinbach and Kumar)

In general, if the given dataset has k sets of items, there will be 2k -1 frequent

sets of items. Since number of sets of items (k) can be very large, the number of

frequent sets of items, 2k -1, will increase exponentially. In another word, the level of

complication is also exponentially growing (Tan, Steinbach and Kumar).

What’s more, if we want to determine the support count for all candidate sets

of items in the lattice structure, the brute-force approach is one of the essential

technologies for us. According to this method, we need to compare each candidate

frequent set of items against every transaction. Figure 3.2 shows the whole operations.

20

Figure 3.2 Brute-‐force Approach Operations (Tan, Steinbach and Kumar)

As a very detailed and straightforward method, brute-force approach can help

us to find the best combinations in most situations. It is also a simple approach.

However, just like we mentioned before, the increasing number of itemsets will

complicates the computation exponentially. Therefore, brute-force approach is only

good for the study with a small scale of dataset.

When we have a large scale of dataset, we always prefer the Apriori

Algorithm approach. “Apriori is the first association rule mining algorithm that

pioneered the use of support-based pruning to systematically control the exponential

growth of candidate itemsets” (Tan, Steinbach and Kumar). It is a better method to

lower the computation’s level of complex. The operations of aprior algorithm

approach are showed as following:

Table 3.3 Original Sample Data

Minimum Support Count = 3

Transaction ID Items

1 {A, B, C, D}

2 {A, B, C, E}

3 {A, B, D}

4 {B, D}

5 {A, B, B}

21

Table 3.4 Candidate 1-itemsets

Step 1: Count the number of transactions

for each item. (Note: even product B is

bought 6 times, it only occurs in 5

transactions)

Table 3.5 Candidate 1-itemsets-cont

Step 2: Discard the candidate itemsets if

the number of transactions is fewer than

minimum support count.


Step 3: Make pairs of all products in table

4.5. Then count the number of purchases

for each pair.

Table 3.7 Candidate 2-itemsets-cont

Step 4: Repeat the step 2.


Step 5: Repeat step 3 and step 4.

Item No. of transaction

A 4

B 5

C 2

D 3

E 1

Item No. of transaction

A 4

B 5

D 3

Itemset No. of transaction

AB 4

AD 2

BD 3


AB 4

BD 3


ABD 2

22

The example above shows how the apriori algorithm approach controls the

exponentially growing in the number of candidate itemsets. The explanation of apriori

property shows us, “all subsets of a frequent item-set must also be frequent” (Tan,

Steinbach and Kumar).

If we already know an itemset is infrequent, there is no need to study its

subsets because they are also infrequent. Therefore, the apriori algorithm approach is

more efficient than the brute-force approach for large-scale of dataset in frequent

itemsets mining.

3.2.4 Association Rules Generation The second step in association rule generation is generating the exact

association rules based on the given frequent itemsets (Han and Kamber).

Each frequent itemset, Y, can produce up to 2k – 2 association rules. An

association rule can be extracted by partitioning the itemset Y into two non-empty

subsets, X and Y – X, such that X à Y – X satisfies the confidence threshold (Tan,

Steinbach and Kumar; Han and Kamber).

For instance, if X= {A, B, C}, it can produce up to 23 – 2 = 6 association rules.

All possible association rules are listed:

{A} à {B, C}

{B} à {A. C}

{C} à {A, B}

{A, B} à {C}

{A. C} à {B}

{B, C} à {A}

Apriori Algorithm can also be used in association rules generation. As a level-

wise approach, “each level corresponds to the number of items that belong to the rule

consequent” (Pang-Ning, 2006). The high-confidence rules are used to generate the

new rules by merging or discarding.

For instance, if we set {A, B, C} à {D} and {A, B, D} à {C} are high-

confidence rules, then the new rules can be generated by merging: {A, B} à {D, C}.

If we set {B, C, D} à {A} is a low-confidence rule, then all the rules includes {A}

can be discarded.

23

Figure 3.3 Apriori Algorithm Rule Generation (Lanzi, 2009)

The graph above visualized shows what happens after apriori

algorithm detect the unsatisfied rules by correlations. If one rule is determined

to useless, then all the other included rules will be also determined useless

without any calculation needed. That can directly save time during the large-

scale of dataset analysis.

24

Chapter 4: Data analysis and results

4.1 Market Basket Analysis Market basket analysis allows researchers to know more about the customer

behaviors and the sales pattern by analyzing the historical transactions. The basic data

adjustment has been completed in Excel.

For the frequent itemsets generating and association rules generating

procedures, we will use the apriori algorithm method by the selected analysis package.

And the minimum support will be set as 10%, the minimum confidence will be set as

80%, the minimum lift will be set as 1.

4.1.1 Analysis Package Selection -- SPSS There are several software can be used in data analysis research. For instance,

SPSS, R, SAS, Excel and Matlab are all good packages for data analysis. All of them

have their own advantages in analysis. The table 4.1 shows more detailed

comparative information (Connor)

Table 4.1 Comparison of data analysis software

Name Advantages Limitations Open Source

SPSS Large datasets

Visualization

Statistical analysis

Expensive No

R Library Support

Statistical analysis

Programming-oriented

Improper learning curve

Yes

Excel Easy to use

Complete function

Poor performance in large

datasets

No

Matlab Elegant Matrix

Support

Programming-oriented

Poor statistical ability

No

Based on that, we know SPSS is our best choice in this study. Although Excel

is an easy and full function package for data analysis, we cannot choose it since its

poor performance in large dataset. For the other two packages, R and Matlab, we

25

have to give up them because of their weak visualization and programming-oriented

characteristics.

As a predictive analytics software, SPSS has the abilities in data collection,

statistics, modeling and deployment (IBM). It can not only build predictive models

based on the data collection, but also provide detailed statistical analysis visualized.

Therefore, for this study, we decided to use SPSS to determine what kinds of flavor

products will be purchased together in the specified region.

4.1.2 SPSS Modeling Process The following figures show how to build an association rules algorithm in

SPSS. All six-research questions mentioned in preview chapter can be answered by

loading different classified datasets into this model.

Figure 4.2 SPSS Modeling Explanation 1

Step 1: Load the Excel source.

Step 2: Assign a “Type” to the loaded Excel source.

Changing all roles of product to “Both”, which means the roles of the fields

are both input (predictor) and target (predicted). Then changing all products’

measurement level to “Flag” since we are using “0” and “1” in data.

26


Step 3: Add an “Apriori” model to the Type.

Setting the minimum antecedent support to 10%, the minimum rule

confidence to 80% and the maximum number of antecedents to 2 (products). The

reason why we control the maximum number of antecedents to two is limiting the

number of generated rules. If we allow more antecedents, more rules will be

generated. That will increase the difficulty level of mining rules.

Step 4: Run the Apriori Node Model to get the results.

The results includes all rules satisfied 10% minimum support and 80%

maximum confidence. We can sort the list by either support or confidence to do

further analysis.

27


Step 5: Generate the rule sets based on the results.

Set the target field as each product, minimum support as 10%, minimum

confidence as 90%, and default value as 0. Then we will get the scoring of

association rules. Setting the minimum confidence as 90% when we generating

ruleset can decrease the difficulty of mining rulesets. Since we don’t need to analyze

all rules, only the rules have high confidence need to be focused. A higher minimum

confidence setting can help us more focus on the valuable rules.

What’s more, we can also use Association Rules Node to generate rules. The

steps are same as the Apriori Node.

28

4.2 Results

4.2.1 Result for All Regions Figure 4.6 Levels of Link between Two Products

Figure 4.6 gives us a graphical understanding about levels of products links.

The heavier line it shows, the stronger links. It provides us some one-to-one

relationships between products. Some strong links such as Chives Flavor and Sauced

Beef Flavor, Crab Flavor and Cheese Corn Flavor, Chives Flavor and Yolk Flavor

are visualized in this figure.

Table 4.2 Rule Statistics in all regions

Measurements Minimum Maximum Mean Stand.

Deviation

Condition Support 10.59 % 55.29% 29.04% 8.76%

Confidence 80.00% 100.00% 88.55% 5.35%

Rule Support 10.00% 48.82% 25.58% 7.42%

Lift 1.45 2.89 1.82 0.24

Deployability 0.00% 9.41% 3.47% 2.09%

Number of Rules: 365

29

Table 4.3 Information for Most Frequent Items

Item Name Records (%) Conditions (%) Predictions (%)

Curry beef 55.29 13.7 12.05

Sauced beef flavor 54.12 14.79 14.25

Pepper beef steak 54.12 13.7 8.49

Hot & spices salt 51.18 12.05 0.82

Chicken flavor 50.59 12.33 11.51

BBQ flavor 50 13.15 12.88

Cumin BBQ 47.06 15.62 13.42

Kimchi flavor 45.88 18.36 9.32

Tomato flavor 44.12 3.56 0.82

Yolk 44.12 13.15 5.75

Hot&spice sichuan 43.53 14.25 2.19

Cheese corn flavor 41.18 3.29 1.64

Pork steak 37.65 19.45 4.93

Chives flavor 31.76 21.37 1.64

Crab flavor 30.59 3.29 0.27

Table 4.2 shows us the rule statistics about the rules. It gives us a basic

understanding of the generated rules. There are 365 rules were generated, so we need

to do the scoring to find out the most valuable or interesting rules.

Table 4.3 provides us what products are popular in the original dataset, and

what products will be popular in future based on the prediction. For instance, Curry

Beef Flavor exists in 55.29% records. It is the most popular flavor based on the

original dataset. What’s more, Sauced Beef Flavor has the highest prediction

percentage, which means 14.25% rules include this flavor. In another word, Sauced

Beef Flavor may be the most popular product in future. Therefore, the company

should more focused on these high prediction products by promoting to increase the

sales or inventory and production cycling time control. We will also more focused on

analyzing the rules include these products. It is also a strong evidence for the

following valuable rules generation.

30

Table 4.4 Most Interesting Association Rules for All Regions (2013 – 2015)

Probability (%) Antecedent Consequent

Rule 1 1 Chives Flavor and Pepper Beef

Steak

Sauced beef flavor

Rule 2 0.909 Cheese Corn Flavor and

Chives Flavor

Yolk flavor

Rule 3 0.917 Chives Flavor and Hot Spice

Sichuan

BBQ flavor

Rule 4 0.963 Pork Steak Flavor and Hot

Spice Salt

Curry beef flavor

Rule 5 1 Cheese Corn and Pepper Beef

Steak

Curry beef flavor

Rule 6 0.958 Crab Flavor and BBQ Flavor Cheese corn flavor

Rule 7 0.955 Crab Flavor and Sauced Beef

Flavor

Cheese corn flavor

Rule 8 0.912 Chives Flavor and Pepper Beef

Steak

Curry beef flavor

Rule 9 1 Chives Flavor and Hot Spice

Sichuan

Pork Steak flavor

Rule 10 0.923 Yolk and Cheese Corn Flavor BBQ flavor

Table 4.4 shows us the “most interesting” association rules based on analyzing

the dataset for all regions. The result were scored based the rule sets generation by

targeting each product. (Full result is available in Appendix B, Table 1.)

Choosing the rules with high confidence, high lift, but low support. A rule

satisfied all three conditions usually means the products do not have a high

appearance rate in dataset, but they are associated with each other. In another word,

this rule is unpredictable in previous research. Then, picking up the highest

probability rules related to those high prediction products. The full results of rule sets

generation and scoring are available in Appendix B, Table 2. And the detailed

31

transaction and scoring information in tabular format for top ten rules is also available

in Appendix B, Table 3.

High affinity products:

1. If a customer purchases Crab Flavor and BBQ Flavor at the same time,

there is 95.8% probability that the customer will order Cheese Corn

Flavor.

As one of the most popular flavor in snacks market, BBQ Flavor

already has a lot of mature products nationwide for a long time. What’s more,

in the past three years, Crab Flavor becomes more and more popular in the

nuts and beans end product market. And this flavor gradually expands to other

kinds of end product such as chips or crackers. Both of these two flavors have

good trends in crackers market. Cheese Corn Flavor, as a western taste flavor

usually used in snacks and crackers products, it is highly accepted among

young end product customers in recent years. For the customers who target

the snacks and crackers market, they do have a high probability to purchase all

these three flavors at the same time. Therefore, it is reasonable that Cheese

Corn Flavor will be also ordered at the same time.

2. If a customer purchases Cheese Corn Flavor and Yolk Flavor at the same

time, there is 92.3% probability that the customer will order BBQ Flavor.

Both of Cheese Corn Flavor and Yolk Flavor belong to sweet flavors.

They are usually used in puffed snack, such as Taiwan Pei Tien energy 99

sticks and Japanese rice rolls. However, BBQ Flavor belongs to salty flavors.

In fact, transferring from sweet to salty is the current flavor development

direction of the Chinese flavor market. Hence, this rule perfectly represents

the trend of further flavor development.

The company can exploit the association rules listed above by applying those

in future marketing strategies. The sales can base on these rules to do products

recommendation and promotion. For instance, when a customer orders Cheese Corn

Flavor and Chives Flavor together, there is 90.9% that the customer is also interested

in Yolk Flavor. In order to increase sales, Yolk Flavor can be recommended to the

32

customer by providing samples before asking. What’s more, these rules can be also

used to guide the future internal technology development. The technical department

can pay more attention on improving the quality of those high prediction rate

products. For example, Sauced Beef Flavor, Cumin BBQ Flavor and BBQ Flavor are

the top three prediction rate products, they should be emphasized in the future internal

technical development schedule.

4.2.2 Result for the North China Region

Figure 4.7 Levels of Link between Two Products (North China)

The level of every two flavors connection is showed visualized on figure 4.7.

The following flavor sets have strong linked: Cumin BBQ Flavor and Sauced Beef

Flavor, Cumin BBQ Flavor and Yolk Flavor, Chives Flavor and Yolk Flavor, and

Cheese Corn Flavor and Yolk Flavor.

33

Table 4.5 Rule Statistics (North China)


Deviation

Condition Support 12.50% 59.38% 32.77% 8.70%

Confidence 90.00% 100.00% 95.93% 4.28%

Rule Support 12.50% 56.25% 31.26% 7.95 %

Lift 1.31 2.29 1.86 0.26

Deployability 0.00% 3.13 % 1.51 % 1.56 %


Table 4.6 Information for Most Frequent Items (North Region)

Item name Records (%) Conditions

(%)

Predictions

(%)



Curry beef 59.38 11.19 4.82

Hot & Spice salt 59.38 10.76 1.42



Crab flavor 53.13 13.03 2.97


Hot & Spice Sichuan 53.13 10.48 7.65

BBQ flavor 50.00 12.75 9.21



Cumin BBQ 43.75 17.00 7.22

Pork steak 43.75 17.28 6.80

Yolk 40.63 16.43 5.24

34

Table 4.5 states the rule statistics. There are 395 rules were generated. The

range of support is 12.5% to 59.4%, range of confidence is 90% to 100%, and all

rules have a lift higher than 1.

Table 4.6 answers us the question between products and popularity. Kimchi

flavor is the most popular product in the original dataset, and its prediction percentage

is also the highest. That means Kimchi flavor has the highest probability to be

popular in future. This result is an important reference conditions for the following

valuable rules generation.

Table 4.7 Most Interesting Association Rule for North Region (2013 – 2015)

Antecedent Consequent

Rule 1 Hot & Spice Salt and BBQ Flavor Kimchi Flavor

Rule 2 Yolk and Pepper Beef Steak Flavor Cheese Corn Flavor

Rule 3 Hot & Spice Salt and Pork Steak Flavor BBQ Flavor

Rule 4 Hot & Spice Salt and Chives Flavor Sauced Beef Flavor

Rule 5 Pork Steak and Curry Beef Flavor Cheese Corn Flavor

Rule 6 Yolk and Pepper Beef Steak Kimchi Flavor

Rule 7 Cumin BBQ and Curry Beef Flavor Sauced Beef Flavor

Rule 8 Pork Steak and Curry Beef Flavor Sauced Beef Flavor

Rule 9 Pork Steak and Pepper Beef Steak Flavor BBQ Flavor

Rule 10 Hot & Spice Salt and Chicken Flavor Kimchi Flavor

Table 4.7 lists the top ten “interesting” association rules for the North China

region. Just like section 4.2.1, this result was also based on apriori rule sets

generation and analysis. (Full result is available in Appendix B, Table 4.) We sorted

all rules by both confidence and support. A rule with high confidence but low

support can provide us more unexpected information, which is defined as an

interesting rule. Then picking up the high probability rules especially for high

prediction products. The full results of rule sets generation and scoring are available

in Appendix B, Table 5. And the scores in tabular format are also available in

Appendix B, Table 6.

35


1. If a customer purchases Hot & Spice Sichuan Flavor and Chicken Flavor

at the same time, there is high probability that the customer will order

Kimchi Flavor.

All of these three flavors belong to salty flavors. One of the main uses

of these flavors is jerky production. In fact, the jerky products have a wider

acceptance in this region. What’s more, the consumers in the North China

region prefer strong tastes than other regions. Kimchi Flavor may be the

further trend in jerky market. It can be combined with other flavors such as

Chicken Flavor to develop new product.

Therefore, for the manufactures that target jerky products in North

China region, it is reasonable for them to order these three flavors together.

This rule represents that, in North China region, some manufacturers may start

to develop new Kimchi Flavor end products.

All these rules can be considered in further marketing strategies making. It

provides sales man more interesting understanding for products recommendation. For

instance, when a customer orders Pork Steak Flavor and Curry Beef Flavor together,

the sales man can recommend Sauced Beef Flavor and Cheese Corn Flavor to the

customer. Although these two flavors are not commonly connected, the association

rules do find out the strong relationship between them. What’s more, the rules can

also help the company to improve their current recommendation combination. If a

customer suddenly change his or her usual purchases combination, company need to

realize the change and figure out what happened behind the change. Then, if the

customer changes the purchases because of any market strategies related reason,

company should have the new recommendation based on customers’ current

purchasing preferences.

36

4.2.3 Result for the South China Region

Figure 4.8 Levels of Link between Two Products (South China)

Figure 4.8 shows us the level of connections between two flavors. Cumin BBQ

Flavor, Yolk Flavor, Pork Steak Flavor, Chicken Flavor all have strong connections

to each other.

Table 4.8 Rule Statistics (South China)


Deviation

Condition Support 11.76 % 58.82% 30.29 % 8.70%

Confidence 90.00% 100.00% 96.59% 4.14%

Rule Support 11.76 % 52.94 % 29.07 % 7.97 %

Lift 1.53 3.09 1.95 0.36

Deployability 0.00% 5.88 % 1.21 % 1.47 %


Table 4.9 Information for Most Frequent Items (South China)


(%)

Predictions

(%)


spices salt 58.82 8.36 4.18


37


Curry beef 55.88 8.62 2.61


BBQ flavor 52.94 10.97 10.44


Hot Sichuan flavor 52.94 9.40 8.09

Crab flavor 50.00 10.44 2.09

Chives flavor 50.00 12.27 10.18


Cumin BBQ 38.24 19.84 7.57

Yolk 38.24 19.32 5.48

Pork steak 32.35 20.10 2.35

Table 4.8 shows the basic statistics of the 383 rules. The range of support is

11.8 % to 58.8%, range of confidence is 90% to 100%, and all rules have a lift higher

than 1.

Based on table 4.9, we know that Sauced beef Flavor, Hot & Spice Salt

Flavor, and Kimchi Flavor are the products existing in 58% records. However, in the

prediction, the highest percentage of rules that has BBQ flavor.

Table 4.10 Most Interesting Association Rule for South China (2013 – 2015)


Rule 1 Pork Steak Flavor and Crab Flavor Chicken Flavor

Rule 2 Curry Beef Flavor and Chicken Flavor Cheese Corn Flavor

Rule 3 Yolk Flavor and Crab Flavor BBQ Flavor

Rule 4 Pepper Beef Steak and Chicken Flavor Kimchi Flavor

Rule 5 Cumin BBQ and Pepper Beef Steak BBQ Flavor

Rule 6 Yolk Flavor and Tomato Flavor Sauced Beef Flavor

Rule 7 Hot & Spice Salt and Chives Flavor Kimchi Flavor

Rule 8 Pork Steak Flavor and Crab Flavor Chives Flavor

Rule 9 Hot & Spice Salt and Chicken Flavor Cheese Corn Flavor

Rule 10 Crab Flavor and Cumin BBQ Flavor BBQ Flavor

38

Table 4.10 states the top ten “interesting” association rules for the North

China region. Just like section 4.2.1, this result was also based on apriori rule sets

generation and analysis. (Full result is available in Appendix B, Table 7.) Following

the high confidence and low support conditions, we can pick up the high probability

rules for high prediction products. The full results of rule sets generation and scoring

are available in Appendix B, Table 8. And the scores in tabular format are also

available in Appendix B, Table 9.


1. If a customer purchases Pork Steak Flavor and Crab Flavor at the same

time, there is high probability that the customer will order Chives Flavor.

Pork Steak Flavor is one of the nationwide popular flavors. It can be

used in many different kinds of end products. So the purchasing of this flavor

is more stable than others. What’s more, Crab Flavor, as a fast growing

product, the acceptance of it is keep on increasing especially in South China.

Another product with a strong regional preference is Chives Flavor.

As a new product with growth potential, Chives Flavor becomes more popular

in the South China region. The popularity of this flavor is affected by the

dietary habits in South East Asia. It is one of the target markets for the cracker

manufacturers in South China region.

This rule represent a strong regional preference, which is valuable for

the company to make specific market strategies in this region.

2. If a customer purchases Yolk Flavor and Tomato Flavor at the same time,

there is high probability that the customer will order Sauced Beef Flavor.

Tomato Flavor is a long-history flavor in China. It is also a common

flavor exists in multiply kinds of snacks. Yolk Flavor, a sweet flavor, is one of

the high acceptance products in South China. It can be used in puffed food or

snacks. There is a market for both of these two products in South China

region.

Sauced Beef Flavor is a significant salty flavor, which was very

popular in North China. However, the acceptance of this flavor in South China

keeps on increasing.

39

This rule also represented the current flavor development trend in

China: customer preferences transfer from sweet to salty.

All these rules are useful for the company in marketing decision making.

More hidden information can be applied in products recommendation and promotion.

For instance, when Pork Steak Flavor and Crab Flavor exist in a transaction, there is

a very high probability that Chicken Flavor and Chives Flavor also exist. In another

word, when a customer orders Pork Steak Flavor and Crab Flavor together, he or she

may also want to order Chicken Flavor or Chives Flavor.

4.2.4 Result for the Mid China Region Figure 4.9 Levels of Link between Two Products (Mid China)

Figure 4.9 represent the different level of links between two flavors. In the

grapy, we can see that Chives Flavor, Yolk Flavor, and Pork Steak Flavor all have

strong links to each other. And Cumin BBQ Flavor also has strong links with Chives

Flavor, Chicken Flavor, Yolk Flavor and Pork Steak Flavor.

40

Table 4.11 Rule Statistics (Mid China)


Deviation

Condition Support 12.50 % 56.25% 31.36 % 8.39%

Confidence 90.00% 100.00% 96.95% 4.12%

Rule Support 12.50 % 53.13 % 30.24% 7.72 %

Lift 1.44 2.67 2 0.39

Deployability 0.00% 3.13 % 1.12 % 1.50%


Table 4.12 Information about Most Frequent Items (Mid China)


(%)

Predictions

(%)



spices salt 59.38 7.31 4.72

Kimchi flavor 59.38 12.74 11.79

Hot sichuan 59.38 11.32 9.91

Curry beef 56.25 6.60 2.36



BBQ flavor 46.88 15.09 11.79

Crab flavor 43.75 13.44 0.00


Cumin BBQ 40.63 18.87 10.38

Yolk 40.63 16.98 7.78

Pork steak 37.50 17.69 5.42


41

Table 4.11 shows the basic statistics. There are 424 rules based on the aprior

analysis. The range of support is 12.5 % to 56.25%, range of confidence is 90% to

100%, and all rules have a lift higher than 1.

Table 4.12 states the information about most frequent flavors. Sauced Beef

Flavor has the highest appearance percentage in all 15 flavors. However, the highest

prediction flavors are Kimchi Flavor, BBQ Flavor, and Chicken Flavor.

Table 4.13 Most Interesting Association Rule for Mid Region (2013 – 2015)


Rule 1 Curry Beef and BBQ Flavor Chicken Flavor

Rule 2 Pepper Beef Steak and Cumin BBQ Flavor BBQ Flavor

Rule 3 Hot & Spice Salt and Cumin BBQ Kimchi Flavor

Rule 4 Pork Steak and Crab Flavor Cumin BBQ Flavor

Rule 5 Chives Flavor and Crab Flavor Kimchi Flavor

Rule 6 Crab Flavor and Yolk Flavor Hot & Spice Sichuan

Rule 7 Pork Steak Flavor and Crab Flavor BBQ Flavor

Rule 8 Curry Beef and Chicken Flavor BBQ Flavor

Rule 9 Hot & Spice Salt and BBQ Flavor Chicken Flavor

Rule 10 Crab Flavor and Yolk Flavor Cumin BBQ Flavor

Table 4.13 shows the most “interesting” association rules for the Mid China

region. Like section 4.2.1, apriori rule sets analysis was used to generate it. (Full table

is available in Appendix B, Table 10.) After sorting result from the highest

confidence and lowest support, we can pick up the rules by considering the

percentage of prediction. The full results of rule sets generation and scoring are

available in Appendix B, Table 11. And the scores in tabular format are also available

in Appendix B, Table 12.


1. If a customer purchases Crab Flavor and Pork Steak Flavor at the same

time, there is high probability that the customer will order BBQ Flavor.

Both of the Pork Steak Flavor and BBQ Flavor are typical salty

flavors. And the Crab Flavor as a fast growing flavor becomes more and more

42

popular in multiple regions. All of these three flavors are often used in nuts

and beans products. What’s more, Mid China region distribute a plenty of

small nuts and beans snacks manufacturers. So this region has a wider nut

snacks market than other regions.

This rule can be used for targeting the nuts snacks manufacturers in

the Mid China region.

2. If a customer purchases Yolk Flavor and Crab Flavor at the same time,

there is high probability that the customer will order Hot & Spice Sichuan

Flavor.

Yolk Flavor and Crab Flavor are usually used in snacks production in

the past few years. Even in the Mid China region, especially Hu’nan and

Hu’bei province, which has a fondness for spicy flavors, these two flavors still

have a growing development.

In fact, Hot & Spice Sichuan Flavor is a flavor with a very strong

regional preference. It can only used in jerky or meat products in the past

because of the technology limitation. However, the developing production

technology in today provides more opportunities to this flavor. Several

manufacturers in Mid China already start to launch new nuts even puffed

products with Hot & Spice Sichuan Flavor to satisfy the end-customer in this

region.

For the manufacturers, which focus on snacks production in Mid

China region, they do have a high probability to purchase these three products

at the same time.

By considering the rules, the sales man can provide more targeted

recommendation to customers. For instance, if a customer orders Yolk Flavor and

Crab Flavor at the same time, sales man can recommend to him or her two flavors,

Hot & Spice Sichuan Flavor and Cumin BBQ Flavor based on the rules.

43

4.2.5 Result for the East China Region Figure 4.10 Levels of Link between Two Products (East China)

Figure 4.10 shows the level of links between every two flavors based on the

data in Mid China region. Chives Flavor, Chicken Flavor, and Cumin BBQ Flavor

are strongly linked to Pork Steak Flavor. And Cumin BBQ Flavor also links with

Chives Flavor, Chicken Flavor, Yolk Flavor, BBQ Flavor and Pork Steak Flavor.

Table 4.14 Rule Statistics (East China)


Deviation

Condition Support 11.76% 61.76% 29.36% 10.40%

Confidence 90.00% 100.00% 96.55% 4.05%

Rule Support 11.76% 55.88% 28.10% 9.55%

Lift 1.46 2.83 2.02 0.33

Deployability 0.00% 5.88% 1.26% 1.47%


44

Table 4.15 Information for Most Frequent Items (East China)


(%)

Predictions

(%)

Pepper Beef Steak Flavor 61.76 7.71 5.32

Tomato Flavor 58.82 10.64 3.19

Sauced Beef Flavor 55.88 5.85 9.04

Curry Beef Flavor 55.88 9.31 3.72

Hot & Spice Salt Flavor 55.88 5.59 0.53

Kimchi Flavor 55.88 13.03 12.77

Hot & Spice Sichuan Flavor 55.88 9.84 7.45

Cheese Corn Flavor 52.94 10.64 9.04

Crab Flavor 47.06 12.77 1.60

BBQ Flavor 44.12 13.56 11.44

Chicken Flavor 41.18 19.15 9.04

Cumin BBQ Flavor 41.18 18.09 9.84

Yolk Flavor 41.18 13.03 4.79

Chives Flavor 41.18 19.95 8.78

Pork Steak Flavor 35.29 23.94 3.46

Table 4.14 states the basic statistics about the rules based on East China

region dataset. It generated 376 rules by aprior method. The range of support is

11.78 % to 61.76%, range of confidence is 90% to 100%, and all rules have a lift

higher than 1.

Table 4.15 lists all 15 flavors’ predictions and records information. Pepper

Beef Steak Flavor is the most frequent product in dataset. There are 61.78% records

has Pepper Beef Steak Flavor. However, the highest prediction flavors are Kimchi

Flavor, which has a 12.77% prediction.

Table 4.16 Most Interesting Association Rule for East Region (2013 – 2015)


Rule 1 Pork Steak Flavor and Crab Flavor Chicken Flavor

Rule 2 Pork Steak Flavor and Tomato Flavor Kimchi Flavor

45

Rule 3 Chicken Flavor and Tomato Flavor Cumin BBQ Flavor


Rule 5 Hot & Spice Salt and BBQ Flavor Cheese Corn Flavor

Rule 6 Crab Flavor and Yolk Flavor Kimchi Flavor

Rule 7 Pork Steak Flavor and Pepper Beef Steak Sauced Beef Flavor

Rule 8 Chives Flavor and Curry Beef Flavor Kimchi Flavor

Rule 9 Curry Beef Flavor and BBQ Flavor Cheese Corn Flavor

Rule 10 Chives Flavor and Tomato Flavor BBQ Flavor

Table 4.16 lists top ten valuable rules in Mid China region. As same as the

sections before, we used the same method, aprior, to generate the result. (Full table is

available in Appendix B, Table 13.) Considering the correlation variables, we chose

the rules related to high prediction flavors with high confidence but low support. Full

rule sets generation and scoring information is also available in Appendix B, Table 14.

And the scores in tabular format are available in Appendix B, Table 15.


1. If a customer purchases Pork Steak Flavor and Tomato Flavor at the same

time, there is a high probability that the customer will order Kimchi

Flavor.

The popularity trend of Kimchi Flavor is from north to south. In

several provinces close to the North China region such as Shandong, Anhui,

the unique sweet spice taste of Kimchi Flavor can satisfied the customer

demand in this region. And it can be perfectly combined with other typical

salty flavor such as Pork Steak Flavor and Tomato Flavor to develop new end

products. This rule may represent the new flavor innovation in the East China.

2. If a customer purchases Curry Beef Flavor and BBQ Flavor at the same

time, there is a high probability that the customer will order Cheese Corn

Flavor.

Both of Curry Beef Flavor and BBQ Flavor are traditional salty

flavors, which usually used in snacks and biscuit products. They are also the

main base flavor in many chips products. Cheese Corn Flavor, a flavor

46

affected by western taste, is popular among the youth. And it is also an

expressive flavor when combining with other base flavors. In fact, the East

China region, such as Shanghai, is very adaptable to new products. This rule

may also represent the new product innovation trend in snack market.

Using the rules listed above, sales man can provide customers more targeted

products in East China region. For instance, if a customer orders Pork Steak Flavor

and Crab Flavor together, there is a high probability that he or she will be also

interested in Chicken Flavor based on the rules.

4.2.6 Result for the South West China Region Figure 4.11 Levels of Link between Two Products (South West China)

Figure 4.11 shows the flavors’ relationship in South West China. The heavier

line means the stronger relationship between two flavors. Pork Steak Flavor and Yolk

Flavor are strongly linked to each other. What’s more, Yolk Flavor and Pork Steak

Flavor are linked to Chicken Flavor.

47

Table 4.17 Rule Statistics (South West China)

Measurements Minimu

m

Maximum Mean Stand.

Deviation

Condition Support (%) 12.12 54.55 30.67 9.25

Confidence (%) 90.00 100.00 97.19 3.92

Rule Support (%) 12.12 51.52 29.62 8.51

Lift 1.41 2.75 1.90 0.33

Deployability (%) 0.00 3.03 1.05 1.44


Table 4.18 Information for Most Frequent Items (South West China)

Item name Records (%) Conditions (%) Predictions

(%)





Hot & Spices Salt 57.58 9.06 0.97

Curry beef 54.55 9.39 6.15

Chives flavor 54.55 12.30 13.92

Hot sichuan 54.55 10.68 8.74

Crab flavor 48.48 6.15 0.00

BBQ flavor 48.48 15.53 12.94

Cumin BBQ 48.48 14.24 11.33



Pork steak 36.36 24.27 2.59

Yolk 36.36 24.60 5.83

Table 4.17 shows the rules’ basic statistics in the South West China region.

309 rules were generated by aprior method based on the regional dataset. The range

48

of support is 12.12 % to 54.55%, range of confidence is 90% to 100%, and all rules

have a lift higher than 1.

Table 4.18 shows all 15 flavors’ appearance rate and prediction rate based on

the provided data. Sauced Beef Flavor is the most popular flavor in original dataset. It

appeared in 63.64% records. The flavor with highest prediction percentage, 15.21%,

is also Sauced Beef Flavor.

Table 4.19 Most Interesting Association Rule for SouthWest China (2013 – 2015)


Rule 1 Yolk Flavor and Curry Beef Flavor Sauced Beef Flavor

Rule 2 Crab Flavor and Yolk Flavor BBQ Flavor

Rule 3 Pork Steak Flavor and Crab Flavor Chives Flavor

Rule 4 Hot & Spice Salt Flavor & Chicken Flavor BBQ Flavor

Rule 5 Chicken Flavor and Curry Beef Flavor BBQ Flavor

Rule 6 Yolk Flavor and Curry Beef Flavor Cumin BBQ Flavor

Rule 7 Crab Flavor and Chicken Flavor Cumin BBQ Flavor

Rule 8 Hot & Spice Salt Flavor and BBQ Flavor Sauced Beef Flavor


Rule 10 Yolk Flavor and Tomato Flavor Chives Flavor

Table 4.19 shows the top ten interesting rules in South West China region. We

kept on using the aprior method to get the result. (Full table is available in Appendix

B, Table 16.)

In order to choose the most interesting rules, we ranking confidence at first,

then sorting the support from bottom. After that, we picked up the top ten interesting

rules by considering the prediction information. Full rule sets generation and scoring

information is available in Appendix B, Table 17. And the scores in tabular format

are available in Appendix B, Table 18.

49


1. If a customer purchases Yolk Flavor and Curry Beef Flavor at the same

time, there is a high probability that the customer will order Cumin BBQ

Flavor.

The South West China region such as Yunnan province has varieties

quality beef products. Yolk Flavor is an essential flavor in beef products to

raise fresh. Curry Beef Flavor and Cumin BBQ Flavor are usually applied to

beef products. They together form the southwest featured taste. For the

manufacturers targeting beef products in this region, this rule is reasonable

and valuable in flavor recommendation.

The rules provide a better understanding on products relationships in South

West China region. For instance, if Yolk Flavor and Tomato Flavor are ordered

together, Sauced Beef Flavor and Chives Flavor are both good candidates in

recommendation based on the rules.

50

Chapter 5: Conclusions

5.1 General Discussion As a useful data mining technique, Market basket analysis can help up to find

out the uncovering connections between products. And it also used in analyzing the

customer purchasing behaviors. The results can provide more information for

decision-making about marketing strategy, inventory control, R & D development

and cross-sale strategies. The generated rules in this study can be used especially in

making decision on marketing strategy, cross-sale recommendations and future

internal research direction. And the company can also base on the regional results to

figure out the targeted strategies for different regions.

In this research, the number of transaction in regional datasets is as big as

expected. The results may be affected by unknown bias. And that also increase the

level of difficulty in mining valuable and interesting rules. However, the result cannot

provide perfectly prediction to every customers, the interesting rules are only the high

probability rules based on analysis. All generated rules are useful and actionable for

company.

5.2 Academic Contribution To date, market basket problem becomes more and more popular. Association

rule analysis has been sufficiently studied for the past few years. This study is

building on previous findings about mining association rules to provide valuable

information to the company. During the dataset adjustment, using the mean numbers

as the cut-off points to each product instead of a fixed number reduced the error.

Evaluating probability and prediction of all products provides more reasonable

meaning during the most valuable association rules choosing. Classifying the dataset

by region gives the company a better understanding of regional customers’ purchase

behaviors.

Major academic contributions:

• A true or false transaction-based dataset was adjusted from the original

dataset by using mean number for each product.

51

• Level of links between every two products in different regions was

visually represented.

• Association rules were generated with confidence, support, and lift.

• The most interesting rules in different regions were chose by

considering confidence, support, rule set probability, and product

prediction.

5.3 Business Contribution In recent years, more and more companies start to emphasize understanding

customer purchasing behaviors to increase sales. Market basket analysis is not

restricted to academic research. The number of application issues related to

association rules analysis is growing dramatically in multiple fields (Square).

Market basket analysis can help the company know more about their

customers’ purchases behaviors. It shows a lot of hidden connections between

different products. Therefore, decision-maker can implement marketing strategies

efficiently. In this study, all five regions have different rule generation results. In

another word, there is a product preference for different regions. Based on that,

company can make different marketing strategies in different regions to realize the

maximum profit.

5.4 Limitations of the study Even though market basket analysis is straightforward and efficient, it still has

some drawbacks and limitations.

• Raw dataset problems

Since market basket analysis is all based on data. If we are not able to

collect or choose the right and related data, the analysis results will not

provide enough valuable information to us.

• Mining association rules problems

Another major difficulty is the large number of the rules generated.

Most of them are trivial. It is hard to find valuable rules from

thousands rules. In this study, only using support and confidence are

not enough to find the most interesting rules.

52

5.5 Directions for Future Research This study only focused on 15 products sales information from 2013 to 2015.

The datasets for every region are small. There are a lot of hidden information between

products cannot be fully exposed. Future research could collect a larger dataset

includes more products and transactions. It can reduce the data bias and generate

more valuable results.

Additionally, there are more questions can be asked based on the association

rules. This study only considered the regional influence. The future study can pay

more attention on different influence variables, such as seasons, times, type of

customers, and type of products.

53

Bibliography Connor, Brendan. AI and Social Science. 23 Feb 2009. 17 8 2016.

Han, Jiawei and Micheline Kamber. Data Mining: Concepts and Techniques. San

Francisco: Morgan Kaufmann, 2001.

IBM. IBM SPSS Retail Market Basket Analysis. 2009.

Lanzi, Pier Luca. Data Mining: Association Rules. 6 December 2009.

Padoe, Iain. "Chapter 9: Market Basket Analysis and Association Rule." 2006. Iain

Pardoe. 15 8 2016.

<http://www.iainpardoe.com/teaching/dsc433/handouts/chapter9h.pdf>.

Ramageri, Bharati. "Data Mining Techniques and Applications." Indian Journal of

Computer Science and Engineering (n.d.).

Square, Loyalty. Market Basket Analysis. n.d. 20 8 2016.

<http://loyaltysquare.com/mba.php>.

Tan, Pang Ning, Michael Steinbach and Vipin Kumar. Introduction to Data Mining.

Boston: Pearson Addison Wesley, 2005.

54

Appendices

Appendix A: Data Description

Table 1: Abbreviation Descriptions List of Abbreviation

TF Tomato Flavor ��风�调�� CF Crab Flavor ��风�调��

SBF Sauced Beef Flavor 酱��调�� CCF Cheese Corn Flavor ��调�� BQF Barbecue Flavor 烧��调�� PBS Pepper Beef Steak Flavor ��调�� CKF Chicken Flavor 鸡��调�� CBF Curry Beef Flavor �喱��调�� CBQ Cumin Barbecue Flavor ��烧味调�� HSS Hot and Spicy Salt Flavor �盐�调�� PSF Pork Steak Flavor 红烧��调�� YF Yolk Flavor ��调�� KF Kimchi Flavor 韩��调��

CVF Chives Flavor ��调�� HSC Hot and Spicy Sichuan Flavor ��调��

Table 2: Adjusted Dataset

YEAR

REGION

MONTH

TF

CF

SBF

CCF

BQF

PBS

CKF

CBF

CBQ

HSS

PSF

YF

KF

CVF

HSC

2013

North China

Jan. 1 0 1 0 0 1 1 1 1 1 0 0 1 0 0 Feb. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Mar. 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 Apr. 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 May. 0 1 1 0 0 1 1 0 0 0 0 0 0 0 0 Jun. 0 1 1 1 1 0 0 1 1 1 1 1 1 1 1 Jul. 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 Aug. 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 Sep. 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 Oct. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Nov. 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 Dec. 0 0 0 1 1 0 1 1 1 1 1 1 1 1 1

2014

Jan. 0 0 1 0 1 1 1 1 0 0 0 0 0 0 1 Feb. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Mar. 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 Apr. 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 May. 0 0 1 1 0 0 0 0 1 0 0 0 0 0 0 Jun. 0 0 1 0 1 0 1 1 1 1 1 1 0 1 0 Jul. 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 Aug. 1 1 1 1 1 0 1 1 1 1 0 1 1 0 0 Sep. 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 Oct. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1

55

Nov. 1 0 0 1 1 1 1 1 1 1 1 1 1 1 0 Dec. 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1

2015


2013

South Ch

ina


2014


2015


2013

Mid China


2 0 1 4 Jan. 0 0 1 0 1 1 1 1 1 1 1 1 1 1 1

56

Feb. 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Mar. 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 Apr. 1 1 1 0 0 0 0 0 0 0 0 0 1 0 0 May. 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 Jun. 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 Jul. 1 0 1 1 0 1 1 1 1 1 1 1 1 0 1 Aug. 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 Sep. 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 Oct. 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 Nov. 0 0 1 0 1 1 1 1 1 1 1 1 1 1 1 Dec. 1 1 1 1 0 1 1 1 1 1 1 0 1 1 1

2015


2013

East China


2014


2015


2013

South

West

Jan. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 Feb. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Mar. 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 Apr. 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0

57

May. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Jun. 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 Jul. 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 Aug. 1 0 1 1 1 1 1 1 1 1 0 1 0 1 1 Sep. 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 Oct. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Nov. 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 Dec. 1 1 0 1 0 1 1 1 1 1 1 1 0 1 1

2014


2015


58

Appendix B: Results

Table 1 Result for All Regions Consequent Antecedent Support % Confidence % Lift

1 CCF CF and TF 25.556 100 2.571

2 CKF CCF and CVF 12.222 100 2.093

3 CBF CCF and PBS 13.333 100 1.915

4 CKF TF and CBF 10 100 2.093

5 PSF CVF and HSC 18.889 100 2.812

6 SBF CVF and PBS 18.889 100 1.957

7 PSF HSC and Yolk 23.889 100 2.812

8 CBF PSF and HSS 15 100 2.25

9 CBF PSF and KF 26.111 100 2.25

10 CBF PSF and PBS 20.556 100 2.25

11 CBF PSF and CBF 22.778 100 2.25

12 CBF PSF and SBF 26.111 100 2.25

13 SBF PSF and PBS 20.556 100 1.957

14 SBF PBS and BQF 30 100 1.957

15 PBS HSS and CBF 37.222 98.507 1.927

16 CBF KF and BQF 31.667 98.246 2.211

17 SBF BQF and CBF 31.667 98.246 1.922

18 CCF CF 28.889 98.077 2.522

19 CBF CVF and SBF 23.889 97.674 2.198

20 CBF CVF and KF 22.778 97.561 2.195

21 SBF PSF and CBF 22.778 97.561 1.909

22 CBF Yolk and KF 22.778 97.561 2.195

23 CBF CVF and CBF 21.111 97.368 2.191

24 SBF CVF and CBF 21.111 97.368 1.905

25 BQF PSF and PBS 20.556 97.297 2.06

26 CBF CVF and HSC 18.889 97.059 2.184

27 CKF CVF and HSC 18.889 97.059 2.031

28 BQF CVF and HSC 18.889 97.059 2.055

29 CBF CVF and PBS 18.889 97.059 2.184

30 CBF HSS and SBF 35 96.825 1.854

31 BQF PSF and HSS 15 96.296 2.039

32 CBF PSF and HSS 15 96.296 1.844

33 SBF PSF and HSS 15 96.296 1.884

34 CBF PSF and BQF 29.444 96.226 2.165

35 PBS HSS and KF 29.444 96.226 1.883

36 CBF TF and PBS 14.444 96.154 1.841

59

37 CBF PBS and SBF 42.222 96.053 1.839

38 PBS HSS and CBF 27.778 96 1.878

39 CBF HSS and CBF 27.778 96 1.838

40 CCF CF and BQF 13.333 95.833 2.464

41 CBF CVF and HSS 13.333 95.833 2.156

42 CBF CVF and HSS 13.333 95.833 1.835

43 SBF CVF and HSS 13.333 95.833 1.875

44 CBF HSC and KF 26.667 95.833 2.156

45 BQF PSF and SBF 26.111 95.745 2.028

46 SBF HSS and BQF 26.111 95.745 1.873

47 SBF Yolk and CBF 25.556 95.652 1.871

48 CBF CVF and PSF 25 95.556 2.15

49 CCF CF and SBF 12.222 95.455 2.455

50 SBF HSC and CBF 24.444 95.455 1.868

51 PBS HSS and CKF 24.444 95.455 1.868

52 CBF HSS and CKF 24.444 95.455 1.828

53 SBF CBF and CBF 35.556 95.312 1.865

54 CBF CCF and KF 11.667 95.238 1.824

55 SBF Yolk and PBS 23.333 95.238 1.863

56 PBS HSS and SBF 35 95.238 1.863

57 CKF CVF and KF 22.778 95.122 1.991

58 BQF PSF and CBF 22.778 95.122 2.014

59 SBF CBF and PBS 33.889 95.082 1.86

60 CCF CF and CKF 11.111 95 2.443

61 CCF CF and CBF 10.556 94.737 2.436

62 SBF CF and CBF 10.556 94.737 1.854

63 CBF PBS and BQF 30 94.444 1.809

64 CBF HSS and KF 29.444 94.34 1.807

65 Yolk CVF and HSC 18.889 94.118 2.259

66 BQF CVF and PBS 18.889 94.118 1.993

67 CBF HSC and BQF 27.778 94 2.115

68 BQF CVF and Yolk 27.222 93.878 1.988

69 BQF CVF and CKF 27.222 93.878 1.988

70 SBF KF and CBF 36.111 93.846 1.836

71 CBF KF and PBS 35 93.651 1.793

72 BQF CVF and CBF 26.111 93.617 1.982

73 BQF PSF and KF 26.111 93.617 1.982

74 CBF CBF and PBS 33.889 93.443 1.789

75 Yolk CVF and PSF 25 93.333 2.24

76 CKF CVF and PSF 25 93.333 1.953

77 BQF CVF and PSF 25 93.333 1.976

60

78 PBS HSC and CBF 24.444 93.182 1.823

79 KF HSS and CKF 24.444 93.182 2.15

80 SBF CKF and CBF 32.222 93.103 1.822

81 BQF CVF and SBF 23.889 93.023 1.97

82 SBF KF and BQF 31.667 92.982 1.819

83 CBF BQF and CBF 31.667 92.982 2.092

84 CBF Yolk and PBS 23.333 92.857 1.778

85 CBF PBS and CKF 30.556 92.727 1.776

86 SBF PBS and CKF 30.556 92.727 1.814

87 BQF CVF and KF 22.778 92.683 1.963

88 PSF Yolk and KF 22.778 92.683 2.607

89 BQF Yolk and KF 22.778 92.683 1.963

90 BQF CVF 30 92.593 1.961

91 PBS PSF and HSS 15 92.593 1.812

92 CBF PBS and BQF 30 92.593 2.083

93 CBF KF and SBF 36.667 92.424 1.77

94 BQF CCF and Yolk 14.444 92.308 1.955

95 Yolk CVF and CBF 21.111 92.105 2.211

96 BQF CVF and CBF 21.111 92.105 1.95

97 SBF KF and PBS 35 92.063 1.801

98 Yolk CVF and BQF 27.778 92 2.208

99 CKF CVF and BQF 27.778 92 1.926

100 SBF HSS and CBF 27.778 92 1.8

101 CBF PSF and PBS 20.556 91.892 1.76

102 CBF HSC and SBF 27.222 91.837 2.066

103 BQF HSC and SBF 27.222 91.837 1.945

104 PBS CVF and HSS 13.333 91.667 1.793

105 BQF CVF and HSS 13.333 91.667 1.941

106 CBF HSS and PBS 40 91.667 1.755

107 PSF CVF and CBF 26.111 91.489 2.573

108 Yolk CVF and CBF 26.111 91.489 2.196

109 CKF CVF and CBF 26.111 91.489 1.915

110 CBF HSS and BQF 26.111 91.489 1.752

111 KF CKF and CBF 32.222 91.379 2.109

112 CKF KF and BQF 31.667 91.228 1.909

113 CBF CVF and PBS 18.889 91.176 1.746

114 BQF PSF and CBF 31.111 91.071 1.929

115 SBF HSS and CBF 37.222 91.045 1.781

116 Yolk CCF and CVF 12.222 90.909 2.182

117 BQF CCF and CVF 12.222 90.909 1.925

118 SBF HSS and CKF 24.444 90.909 1.779

61

119 KF PBS and CKF 30.556 90.909 2.098

120 PBS KF and CBF 36.111 90.769 1.776

121 SBF CBF and BQF 36.111 90.769 1.776

122 Yolk CVF 30 90.741 2.178

123 CKF CVF 30 90.741 1.899

124 CBF PSF and CKF 30 90.741 2.042

125 CKF CVF and SBF 23.889 90.698 1.898

126 CBF KF and CKF 35.556 90.625 2.039

127 CKF CCF and KF 11.667 90.476 1.894

128 SBF CCF and KF 11.667 90.476 1.77

129 CKF TF and KF 11.667 90.476 1.894

130 CBF TF and KF 11.667 90.476 1.733

131 CKF HSC and CBF 28.889 90.385 1.892

132 CBF HSC and CKF 28.889 90.385 2.034

133 BQF HSC and CBF 28.889 90.385 1.914

134 KF CKF and SBF 34.444 90.323 2.084

135 PSF CVF and KF 22.778 90.244 2.538

136 SBF CVF and KF 22.778 90.244 1.766

137 KF PSF and CBF 22.778 90.244 2.083

138 CBF PBS 51.111 90.217 1.728

139 TF CF and CCF 28.333 90.196 2.165

140 PSF Yolk and CBF 28.333 90.196 2.537

141 BQF Yolk and CBF 28.333 90.196 1.91

142 SBF HSC and BQF 27.778 90 1.761

143 CKF CVF and Yolk 27.222 89.796 1.879

144 Yolk CVF and CKF 27.222 89.796 2.155

145 CBF Yolk and SBF 27.222 89.796 1.719

146 CBF CBF and SBF 37.778 89.706 1.718

147 CKF HSC and KF 26.667 89.583 1.875

148 KF CVF and CBF 21.111 89.474 2.065

149 CKF CVF and CBF 21.111 89.474 1.873

150 PBS BQF and CBF 31.667 89.474 1.751

151 CBF KF and SBF 36.667 89.394 2.011

152 CBF BQF and SBF 36.667 89.394 2.011

153 SBF CVF and CBF 26.111 89.362 1.748

154 CKF PSF and KF 26.111 89.362 1.87

155 SBF HSC and PBS 26.111 89.362 1.748

156 PBS HSS and BQF 26.111 89.362 1.748

157 KF CBF and CKF 36.111 89.231 2.059

158 KF PSF and PBS 20.556 89.189 2.058

159 CBF SBF 51.111 89.13 1.707

62

160 KF CBF and CBF 35.556 89.062 2.055

161 PBS CBF and CBF 35.556 89.062 1.743

162 PBS CBF and SBF 45.556 89.024 1.742

163 KF PSF and HSS 15 88.889 2.051

164 CBF CKF and SBF 34.444 88.71 1.996

165 SBF HSS and KF 29.444 88.679 1.735

166 KF CBF and PBS 33.889 88.525 2.043

167 TF CF 28.889 88.462 2.123

168 CF CCF and TF 28.889 88.462 3.062

169 CKF CCF and Yolk 14.444 88.462 1.852

170 KF HSC and CBF 28.889 88.462 2.041

171 PSF CVF and SBF 23.889 88.372 2.485

172 Yolk CVF and SBF 23.889 88.372 2.121

173 CKF HSC and Yolk 23.889 88.372 1.85

174 PBS CBF 52.222 88.298 1.728

175 KF CVF and HSC 18.889 88.235 2.036

176 SBF CVF and HSC 18.889 88.235 1.726

177 Yolk CVF and PBS 18.889 88.235 2.118

178 KF CVF and PBS 18.889 88.235 2.036

179 CKF CVF and PBS 18.889 88.235 1.847

180 PBS Yolk and HSS 18.889 88.235 1.726

181 CBF Yolk and HSS 18.889 88.235 1.69

182 CBF CVF and BQF 27.778 88 1.98

183 KF HSS and CBF 27.778 88 2.031

184 SBF PBS and CBF 46.111 87.952 1.721

185 PBS CKF and CBF 32.222 87.931 1.72

186 PBS KF and SBF 36.667 87.879 1.719

187 Yolk CVF and KF 22.778 87.805 2.107

188 CVF Yolk and KF 22.778 87.805 2.927

189 SBF Yolk and KF 22.778 87.805 1.718

190 CBF CVF and Yolk 27.222 87.755 1.974

191 CBF CVF and CKF 27.222 87.755 1.974

192 CBF Yolk and SBF 27.222 87.755 1.974

193 CBF KF and CBF 36.111 87.692 1.973

194 CBF PSF 35.556 87.5 1.969

195 TF CF and BQF 13.333 87.5 2.1

196 Yolk CVF and HSS 13.333 87.5 2.1

197 KF CVF and HSS 13.333 87.5 2.019

198 CKF PSF and CBF 31.111 87.5 1.831

199 BQF HSC and KF 26.667 87.5 1.853

200 SBF HSC and KF 26.667 87.5 1.712

63

201 SBF KF and CKF 35.556 87.5 1.712

202 SBF CBF 52.222 87.234 1.707

203 SBF PSF and KF 26.111 87.234 1.707

204 KF PSF and SBF 26.111 87.234 2.013

205 CBF HSC and PBS 26.111 87.234 1.67

206 CBF KF 43.333 87.179 1.962

207 CBF CCF and SBF 17.222 87.097 1.668

208 CBF CKF and SBF 34.444 87.097 1.668

209 CBF CVF 30 87.037 1.958

210 CBF Yolk and CBF 25.556 86.957 1.957

211 PSF CVF and CBF 21.111 86.842 2.442

212 CBF PSF and Yolk 29.444 86.792 1.953

213 CKF PSF and Yolk 29.444 86.792 1.817

214 CKF PSF and BQF 29.444 86.792 1.817

215 SBF KF and CBF 37.778 86.765 1.698

216 KF CBF and SBF 37.778 86.765 2.002

217 BQF CBF and SBF 37.778 86.765 1.837

218 CBF PSF and HSC 28.889 86.538 1.947

219 PSF HSC and CBF 28.889 86.538 2.434

220 SBF HSC and CBF 28.889 86.538 1.693

221 KF HSC and CBF 24.444 86.364 1.993

222 CBF HSC and CBF 24.444 86.364 1.943

223 BQF HSC and CBF 24.444 86.364 1.829

224 PBS TF and CBF 16.111 86.207 1.687

225 KF CBF and BQF 36.111 86.154 1.988

226 BQF CBF and CKF 36.111 86.154 1.824

227 CKF CBF and BQF 36.111 86.154 1.803

228 CBF CKF and BQF 36.111 86.154 1.938

229 KF CVF and SBF 23.889 86.047 1.986

230 CBF CVF and SBF 23.889 86.047 1.648

231 CBF HSC and Yolk 23.889 86.047 1.936

232 PSF HSC and BQF 27.778 86 2.419

233 CBF KF and BQF 31.667 85.965 1.646

234 KF BQF and CBF 31.667 85.965 1.984

235 PBS TF and KF 11.667 85.714 1.677

236 SBF TF and KF 11.667 85.714 1.677

237 PSF CVF and Yolk 27.222 85.714 2.411

238 PSF CVF and CKF 27.222 85.714 2.411

239 KF HSC and SBF 27.222 85.714 1.978

240 PBS HSC and SBF 27.222 85.714 1.677

241 CBF HSC and SBF 27.222 85.714 1.641

64

242 BQF Yolk and SBF 27.222 85.714 1.815

243 CBF KF and PBS 35 85.714 1.929

244 BQF Yolk and CKF 30.556 85.455 1.81

245 Yolk PSF and CBF 22.778 85.366 2.049

246 PBS HSC and HSS 22.778 85.366 1.67

247 CKF Yolk and KF 22.778 85.366 1.787

248 CBF Yolk and KF 22.778 85.366 1.635

249 PSF CVF and PBS 18.889 85.294 2.399

250 SBF Yolk and HSS 18.889 85.294 1.669

251 CKF KF and CBF 37.778 85.294 1.785

252 PBS CBF and SBF 37.778 85.294 1.669

253 CBF TF and SBF 15 85.185 1.631

254 Yolk PSF and CKF 30 85.185 2.044

255 BQF PSF and CKF 30 85.185 1.804

256 KF CVF and CBF 26.111 85.106 1.964

257 CKF PSF and SBF 26.111 85.106 1.781

258 CBF PSF and SBF 26.111 85.106 1.63

259 CBF HSS and BQF 26.111 85.106 1.915

260 KF CBF 44.444 85 1.962

261 SBF CBF 44.444 85 1.663

262 BQF CF and CKF 11.111 85 1.8

263 SBF PSF and BQF 29.444 84.906 1.661

264 CKF KF and SBF 36.667 84.848 1.776

265 CBF BQF and SBF 36.667 84.848 1.625

266 PBS Yolk and CBF 25.556 84.783 1.659

267 SBF KF 43.333 84.615 1.656

268 CKF PSF and HSC 28.889 84.615 1.771

269 PSF HSC and CKF 28.889 84.615 2.38

270 SBF CBF and CKF 36.111 84.615 1.656

271 CBF CKF and CBF 32.222 84.483 1.901

272 SBF CVF and PSF 25 84.444 1.652

273 CKF PSF 35.556 84.375 1.766

274 CVF Yolk and CBF 28.333 84.314 2.81

275 SBF Yolk and CBF 28.333 84.314 1.65

276 CKF TF and Yolk 13.889 84 1.758

277 PSF CVF and BQF 27.778 84 2.362

278 KF HSC and BQF 27.778 84 1.938

279 CKF HSC and BQF 27.778 84 1.758

280 KF PSF and CBF 31.111 83.929 1.937

281 SBF PSF and CBF 31.111 83.929 1.642

282 BQF CKF and SBF 34.444 83.871 1.776

65

283 CBF KF and CBF 37.778 83.824 1.605

284 HSC PSF and PBS 20.556 83.784 2.038

285 BQF HSC and Yolk 23.889 83.721 1.773

286 PSF Yolk and CKF 30.556 83.636 2.352

287 CBF PBS and CKF 30.556 83.636 1.882

288 PSF CVF 30 83.333 2.344

289 CBF KF 43.333 83.333 1.596

290 KF TF and CBF 10 83.333 1.923

291 BQF TF and CBF 10 83.333 1.765

292 CKF CVF and HSS 13.333 83.333 1.744

293 CBF Yolk and PBS 23.333 83.333 1.875

294 SBF HSS and PBS 40 83.333 1.63

295 KF PBS and BQF 30 83.333 1.923

296 BQF PSF and Yolk 29.444 83.019 1.758

297 Yolk PSF and BQF 29.444 83.019 1.992

298 KF PSF and BQF 29.444 83.019 1.916

299 CBF HSS and KF 29.444 83.019 1.868

300 HSC PSF and KF 26.111 82.979 2.018

301 CBF CVF and KF 22.778 82.927 1.588

302 PBS PSF and CBF 22.778 82.927 1.622

303 CKF PSF and CBF 22.778 82.927 1.736

304 Yolk PSF 35.556 82.812 1.988

305 BQF PSF 35.556 82.812 1.754

306 CBF KF and CKF 35.556 82.812 1.586

307 BQF CBF and CBF 35.556 82.812 1.754

308 PBS HSS 48.333 82.759 1.619

309 Yolk PSF and HSC 28.889 82.692 1.985

310 BQF PSF and HSC 28.889 82.692 1.751

311 KF HSC and CKF 28.889 82.692 1.908

312 SBF PBS 51.111 82.609 1.616

313 PBS SBF 51.111 82.609 1.616

314 BQF Yolk and CBF 25.556 82.609 1.749

315 CKF Yolk and BQF 31.667 82.456 1.726

316 CKF Yolk and CBF 28.333 82.353 1.724

317 BQF KF and CBF 37.778 82.353 1.744

318 PBS CKF and SBF 34.444 82.258 1.609

319 KF CVF and PSF 25 82.222 1.897

320 Yolk PSF and CBF 31.111 82.143 1.971

321 CKF KF 43.333 82.051 1.717

322 BQF CBF and PBS 33.889 81.967 1.736

323 CBF CF and SBF 12.222 81.818 1.567

66

324 CBF HSS and CKF 24.444 81.818 1.841

325 PBS BQF and SBF 36.667 81.818 1.601

326 CKF HSC and SBF 27.222 81.633 1.709

327 PBS Yolk and SBF 27.222 81.633 1.597

328 PBS CVF and CBF 21.111 81.579 1.596

329 CKF KF and CBF 36.111 81.538 1.707

330 CBF CBF and BQF 36.111 81.538 1.561

331 HSC PSF and CKF 30 81.481 1.982

332 HSC PSF 35.556 81.25 1.976

333 CKF CBF 44.444 81.25 1.701

334 BQF CBF 44.444 81.25 1.721

335 PSF HSC and KF 26.667 81.25 2.285

336 BQF KF and CKF 35.556 81.25 1.721

337 HSC PSF and Yolk 29.444 81.132 1.973

338 HSC PSF and BQF 29.444 81.132 1.973

339 Yolk PSF and PBS 20.556 81.081 1.946

340 CKF PSF and PBS 20.556 81.081 1.697

341 HSS TF and KF 11.667 80.952 1.675

342 BQF Yolk and PBS 23.333 80.952 1.714

343 HSS KF and PBS 35 80.952 1.675

344 CKF CBF and SBF 37.778 80.882 1.693

345 CVF PSF and SBF 26.111 80.851 2.695

346 HSC PSF and SBF 26.111 80.851 1.967

347 Yolk PSF and KF 26.111 80.851 1.94

348 Yolk PSF and SBF 26.111 80.851 1.94

349 KF HSC and PBS 26.111 80.851 1.866

350 CBF HSC and PBS 26.111 80.851 1.819

351 BQF HSC and PBS 26.111 80.851 1.712

352 PBS KF 43.333 80.769 1.58

353 HSS TF and PBS 14.444 80.769 1.671

354 BQF HSC and CKF 28.889 80.769 1.71

355 CVF Yolk and BQF 31.667 80.702 2.69

356 CBF Yolk and BQF 31.667 80.702 1.816

357 CVF PSF and CBF 22.778 80.488 2.683

358 CBF HSC and HSS 22.778 80.488 1.541

359 HSC PSF and CBF 31.111 80.357 1.955

360 BQF KF and SBF 36.667 80.303 1.701

361 KF BQF and SBF 36.667 80.303 1.853

362 CBF CBF 44.444 80 1.532

363 TF CF and CKF 11.111 80 1.92

364 CVF Yolk and CKF 30.556 80 2.667

67

365 SBF CVF and BQF 27.778 80 1.565

366 BQF HSS and CBF 27.778 80 1.694

367 KF CKF and BQF 36.111 80 1.846

368 SBF CKF and BQF 36.111 80 1.565

369 SBF CVF 30 79.63 1.558

370 KF CVF and CKF 27.222 79.592 1.837

371 SBF CVF and CKF 27.222 79.592 1.557

372 HSS PBS and CBF 46.111 79.518 1.645

373 SBF CCF and CBF 18.889 79.412 1.554

374 PBS KF and CBF 37.778 79.412 1.554

375 CKF KF and PBS 35 79.365 1.661

376 SBF TF and CBF 16.111 79.31 1.552

377 CVF PSF and Yolk 29.444 79.245 2.642

378 CVF PSF and BQF 29.444 79.245 2.642

379 SBF CCF and PBS 13.333 79.167 1.549

380 PSF CVF and HSS 13.333 79.167 2.227

381 PBS HSC and KF 26.667 79.167 1.549

382 CBF HSC and KF 26.667 79.167 1.516

383 PBS CVF and SBF 23.889 79.07 1.547

384 HSS PBS and SBF 42.222 78.947 1.633

385 PBS KF and BQF 31.667 78.947 1.545

386 CKF BQF and CBF 31.667 78.947 1.652

387 CKF BQF and SBF 36.667 78.788 1.649

388 CVF PSF and KF 26.111 78.723 2.624

389 CBF CVF and CBF 26.111 78.723 1.507

390 CBF PSF and KF 26.111 78.723 1.507

391 PBS PSF and SBF 26.111 78.723 1.54

392 HSS CBF and PBS 33.889 78.689 1.628

393 PSF CBF and BQF 36.111 78.462 2.207

394 KF Yolk and CBF 28.333 78.431 1.81

395 CBF Yolk and CBF 28.333 78.431 1.502

396 CVF PSF and PBS 20.556 78.378 2.613

397 HSS PBS 51.111 78.261 1.619

398 PBS KF and CKF 35.556 78.125 1.529

399 HSC Yolk and KF 22.778 78.049 1.898

400 SBF TF and CBF 10 77.778 1.522

401 CVF PSF and CKF 30 77.778 2.593

402 HSC PSF and HSS 15 77.778 1.892

403 Yolk PSF and HSS 15 77.778 1.867

404 KF PSF and CKF 30 77.778 1.795

405 HSS PBS and BQF 30 77.778 1.609

68

406 CKF PBS and BQF 30 77.778 1.628

407 SBF BQF 47.222 77.647 1.519

408 BQF CKF and CBF 32.222 77.586 1.643

409 SBF CVF and Yolk 27.222 77.551 1.517

410 CVF Yolk and SBF 27.222 77.551 2.585

411 PSF HSC and SBF 27.222 77.551 2.181

412 PSF Yolk and SBF 27.222 77.551 2.181

413 BQF CCF and CKF 17.222 77.419 1.639

414 CKF HSS and KF 29.444 77.358 1.619

415 TF CF and SBF 12.222 77.273 1.855

416 CKF HSC and CBF 24.444 77.273 1.617

417 PSF Yolk and BQF 31.667 77.193 2.171

418 PSF KF and BQF 31.667 77.193 2.171

419 CBF HSS 48.333 77.011 1.475

420 CVF CCF and Yolk 14.444 76.923 2.564

421 SBF HSC and CKF 28.889 76.923 1.505

422 HSS KF and CBF 36.111 76.923 1.592

423 PBS CBF and BQF 36.111 76.923 1.505

424 CVF PSF and CBF 31.111 76.786 2.56

425 KF HSS and BQF 26.111 76.596 1.768

426 CKF CBF and CBF 35.556 76.562 1.602

427 CBF BQF 47.222 76.471 1.721

428 CKF BQF 47.222 76.471 1.601

429 CBF Yolk and HSS 18.889 76.471 1.721

430 CBF Yolk and CKF 30.556 76.364 1.718

431 HSS PBS and CKF 30.556 76.364 1.58

432 BQF PBS and CKF 30.556 76.364 1.617

433 KF PBS and SBF 42.222 76.316 1.761

434 CBF PBS and SBF 42.222 76.316 1.717

435 PBS CBF 44.444 76.25 1.492

436 CVF Yolk and CBF 25.556 76.087 2.536

437 PSF Yolk and CBF 25.556 76.087 2.14

438 KF Yolk and CBF 25.556 76.087 1.756

439 BQF Yolk 41.667 76 1.609

440 BQF TF and Yolk 13.889 76 1.609

441 KF CVF and BQF 27.778 76 1.754

442 PBS HSC and BQF 27.778 76 1.487

443 CBF HSC and BQF 27.778 76 1.455

444 KF CVF 30 75.926 1.752

445 HSC PSF and CBF 22.778 75.61 1.839

446 SBF HSC and HSS 22.778 75.61 1.479

69

447 CBF CKF 47.778 75.581 1.701

448 BQF CKF 47.778 75.581 1.601

449 HSC CVF and PSF 25 75.556 1.838

450 HSS BQF and CBF 31.667 75.439 1.561

451 CKF CBF and PBS 33.889 75.41 1.578

452 PSF CBF and CKF 36.111 75.385 2.12

453 BQF KF and CBF 36.111 75.385 1.596

454 CBF CBF and CKF 36.111 75.385 1.444

455 Yolk CF and CKF 11.111 75 1.8

456 Yolk CCF and BQF 17.778 75 1.8

457 CKF CCF and BQF 17.778 75 1.57

458 CKF TF and HSC 11.111 75 1.57

459 KF PSF and HSC 28.889 75 1.731

460 HSS HSC and CBF 24.444 75 1.552

461 HSS CBF and CBF 35.556 75 1.552

462 KF HSS and CBF 37.222 74.627 1.722

463 KF HSS and SBF 35 74.603 1.722

464 HSS HSC and PBS 26.111 74.468 1.541

465 KF CKF 47.778 74.419 1.717

466 CVF HSC and Yolk 23.889 74.419 2.481

467 KF HSC and Yolk 23.889 74.419 1.717

468 HSS CBF and SBF 45.556 74.39 1.539

469 KF CBF and SBF 45.556 74.39 1.717

470 CBF CBF and SBF 45.556 74.39 1.674

471 TF CCF 38.889 74.286 1.783

472 Yolk CCF and CKF 17.222 74.194 1.781

473 CKF TF and BQF 17.222 74.194 1.553

474 CKF PSF and HSS 15 74.074 1.55

475 SBF PSF and CKF 30 74.074 1.449

476 CBF SBF 51.111 73.913 1.663

477 TF CF and CBF 10.556 73.684 1.768

478 PBS CF and CBF 10.556 73.684 1.442

479 HSC KF and BQF 31.667 73.684 1.792

480 SBF Yolk and BQF 31.667 73.684 1.442

481 CBF PSF and BQF 29.444 73.585 1.409

482 BQF Yolk and HSS 18.889 73.529 1.557

483 KF CVF and Yolk 27.222 73.469 1.695

484 KF Yolk and SBF 27.222 73.469 1.695

485 KF PSF 35.556 73.438 1.695

486 SBF PSF 35.556 73.438 1.437

487 CKF Yolk 41.667 73.333 1.535

70

488 CBF CVF and PSF 25 73.333 1.404

489 CBF PSF and CBF 31.111 73.214 1.402

490 HSC CVF and KF 22.778 73.171 1.78

491 PBS CVF and KF 22.778 73.171 1.432

492 PBS Yolk and KF 22.778 73.171 1.432

493 BQF KF 43.333 73.077 1.548

494 SBF TF and PBS 14.444 73.077 1.43

495 SBF PSF and HSC 28.889 73.077 1.43

496 Yolk HSC and CKF 28.889 73.077 1.754

497 PBS HSC and CBF 28.889 73.077 1.43

498 CBF HSC and CBF 28.889 73.077 1.399

499 CBF HSS and SBF 35 73.016 1.643

500 CF CCF 38.889 72.857 2.522

501 CBF CCF and CVF 12.222 72.727 1.636

502 BQF HSS and CKF 24.444 72.727 1.54

503 HSC Yolk and CBF 28.333 72.549 1.765

504 SBF HSS 48.333 72.414 1.417

505 PBS TF and HSS 16.111 72.414 1.417

506 HSS CKF and CBF 32.222 72.414 1.498

507 CKF HSC and PBS 26.111 72.34 1.514

508 HSC CBF and CKF 36.111 72.308 1.759

509 HSC CBF and BQF 36.111 72.308 1.759

510 Yolk CKF and BQF 36.111 72.308 1.735

511 CVF TF and CBF 10 72.222 2.407

512 PSF TF and CBF 10 72.222 2.031

513 HSC TF and CBF 10 72.222 1.757

514 CBF TF and CBF 10 72.222 1.383

515 SBF CKF 47.778 72.093 1.411

516 SBF HSC and Yolk 23.889 72.093 1.411

517 Yolk HSC and BQF 27.778 72 1.728

518 CKF HSS and CBF 27.778 72 1.507

519 CF CCF and BQF 17.778 71.875 2.488

520 BQF TF and CKF 17.778 71.875 1.522

521 KF SBF 51.111 71.739 1.656

522 BQF SBF 51.111 71.739 1.519

523 KF PSF and Yolk 29.444 71.698 1.655

524 SBF PSF and Yolk 29.444 71.698 1.403

525 CBF HSS and CBF 37.222 71.642 1.612

526 CVF CCF and KF 11.667 71.429 2.381

527 PBS CCF and KF 11.667 71.429 1.398

528 BQF CCF and KF 11.667 71.429 1.513

71

529 CBF TF and KF 11.667 71.429 1.607

530 CVF Yolk and PBS 23.333 71.429 2.381

531 CBF CVF and Yolk 27.222 71.429 1.368

532 PSF Yolk and PBS 23.333 71.429 2.009

533 HSS Yolk and PBS 23.333 71.429 1.478

534 KF Yolk and PBS 23.333 71.429 1.648

535 CKF Yolk and SBF 27.222 71.429 1.495

536 BQF HSS and SBF 35 71.429 1.513

537 BQF KF and PBS 35 71.429 1.513

538 HSS CBF 52.222 71.277 1.475

539 HSS KF and SBF 36.667 71.212 1.473

540 Yolk HSC and CBF 28.889 71.154 1.708

541 KF PBS and CBF 46.111 71.084 1.64

542 BQF PBS and SBF 42.222 71.053 1.505

543 CCF TF and BQF 17.222 70.968 1.825

544 CVF CCF and CKF 17.222 70.968 2.366

545 CKF CF and BQF 13.333 70.833 1.483

546 KF HSS and PBS 40 70.833 1.635

547 CVF CKF and BQF 36.111 70.769 2.359

548 PSF CKF and BQF 36.111 70.769 1.99

549 Yolk CBF and BQF 36.111 70.769 1.698

550 PBS CBF and CKF 36.111 70.769 1.385

551 KF HSC and HSS 22.778 70.732 1.632

552 PSF Yolk 41.667 70.667 1.988

553 PBS CCF and CBF 18.889 70.588 1.381

554 PBS CVF and HSC 18.889 70.588 1.381

555 HSC CVF and PBS 18.889 70.588 1.717

556 CBF CVF and HSC 18.889 70.588 1.352

557 PSF HSC and CBF 24.444 70.455 1.982

558 CBF CVF 30 70.37 1.348

559 CCF TF and SBF 15 70.37 1.81

560 HSS TF and SBF 15 70.37 1.456

561 PBS TF and SBF 15 70.37 1.377

562 CKF TF and SBF 15 70.37 1.473

563 CVF PSF and HSS 15 70.37 2.346

564 HSC PBS and BQF 30 70.37 1.712

565 CVF PSF 35.556 70.312 2.344

566 PSF HSC 41.111 70.27 1.976

567 CBF HSC 41.111 70.27 1.581

568 CKF HSC 41.111 70.27 1.471

569 HSC CVF and CBF 26.111 70.213 1.708

72

570 PBS CVF and CBF 26.111 70.213 1.374

571 PBS PSF and KF 26.111 70.213 1.374

572 PSF CBF 44.444 70 1.969

573 CBF CVF and BQF 27.778 70 1.34 Table 2 Scoring for Rule Sets in All Regions

Sauced Beef Flavor Rule Set

Sauced Beef Flavor = 1.000 ID Probability Rule

34 1 IF Chives flavor = True and Pepper beef steak = True THEN 1.000 37 1 IF Pork steak = True and Pepper beef steak = True THEN 1.000 54 1 IF Pepper beef steak = True and BBQ flavor = True THEN 1.000 57 0.982 IF BBQ flavor = True and Curry beef = True THEN 1.000 41 0.976 IF Pork steak = True and Curry beef = True THEN 1.000 38 0.974 IF Chives flavor = True and Curry beef = True THEN 1.000 27 0.963 IF Pork steak = True and Hot & spices salt = True THEN 1.000 24 0.958 IF Chives flavor = True and Hot & spices salt = True THEN 1.000 46 0.957 IF Yolk = True and Curry beef = True THEN 1.000 47 0.957 IF Hot & spices salt = True and BBQ flavor = True THEN 1.000 44 0.955 IF Hot & spice sichuan = True and Curry beef = True THEN 1.000 64 0.953 IF Cumin BBQ = True and Curry beef = True THEN 1.000 42 0.952 IF Yolk = True and Pepper beef steak = True THEN 1.000 61 0.951 IF Cumin BBQ = True and Pepper beef steak = True THEN 1.000 19 0.947 IF Crab flavor = True and Curry beef = True THEN 1.000 65 0.938 IF Kimchi flavor = True and Curry beef = True THEN 1.000 58 0.931 IF Chicken flavor = True and Curry beef = True THEN 1.000 57 0.93 IF Kimchi flavor = True and BBQ flavor = True THEN 1.000 55 0.927 IF Pepper beef steak = True and Chicken flavor = True THEN 1.000 63 0.921 IF Kimchi flavor = True and Pepper beef steak = True THEN 1.000 50 0.92 IF Hot & spices salt = True and Cumin BBQ = True THEN 1.000 67 0.91 IF Hot & spices salt = True and Curry beef = True THEN 1.000 44 0.909 IF Hot & spices salt = True and Chicken flavor = True THEN 1.000 65 0.908 IF Cumin BBQ = True and BBQ flavor = True THEN 1.000vor 21 0.905 IF Cheese corn flavor = True and Kimchi flavor = True THEN 1.000 41 0.902 IF Chives flavor = True and Kimchi flavor = True THEN 1.000 50 0.9 IF Hot & spice sichuan = True and BBQ flavor = True THEN 1.000

Cumin BBQ Flavor Rule Set

Cumin BBQ Flavor = 1.000 ID Probability Rule

27 1 IF Pork steak = True and Hot & spices salt = True THEN 1.000 47 1 IF Pork steak = True and Kimchi flavor = True THEN 1.000

73

37 1 IF Pork steak = True and Pepper beef steak = True THEN 1.000 41 1 IF Pork steak = True and Curry beef = True THEN 1.000 47 1 IF Pork steak = True and Sauced beef flavor = True THEN 1.000 57 0.982 IF Kimchi flavor = True and BBQ flavor = True THEN 1.000 43 0.977 IF Chives flavor = True and Sauced beef flavor = True THEN 1.000 41 0.976 IF Chives flavor = True and Kimchi flavor = True THEN 1.000 41 0.976 IF Yolk = True and Kimchi flavor = True THEN 1.000 38 0.974 IF Chives flavor = True and Curry beef = True THEN 1.000 34 0.971 IF Chives flavor = True and Hot & spice sichuan = True THEN 1.000 34 0.971 IF Chives flavor = True and Pepper beef steak = True THEN 1.000 53 0.962 IF Pork steak = True and BBQ flavor = True THEN 1.000 24 0.958 IF Chives flavor = True and Hot & spices salt = True THEN 1.000 48 0.958 IF Hot & spice sichuan = True and Kimchi flavor = True THEN 1.000 45 0.956 IF Chives flavor = True and Pork steak = True THEN 1.000 50 0.94 IF Hot & spice sichuan = True and BBQ flavor = True THEN 1.000 57 0.93 IF BBQ flavor = True and Curry beef = True THEN 1.000 54 0.926 IF Pepper beef steak = True and BBQ flavor = True THEN 1.000 49 0.918 IF Hot & spice sichuan = True and Sauced beef flavor = True THEN

1.000 54 0.907 IF Pork steak = True and Chicken flavor = True THEN 1.000 64 0.906 IF Kimchi flavor = True and Chicken flavor = True THEN 1.000 52 0.904 IF Hot&spice sichuan = True and Chicken flavor = True THEN 1.000

Pork Steak Flavor Rule Set

Pork Steak Flavor = 1.000 ID Probability Rule

34 1 IF Chives flavor = True and Hot&spice sichuan = True THEN 1.000 43 1 IF Hot&spice sichuan = True and Yolk = True THEN 1.000 41 0.927 IF Yolk = True and Kimchi flavor = True THEN 1.000 47 0.915 IF Chives flavor = True and Cumin BBQ = True THEN 1.000 41 0.902 IF Chives flavor = True and Kimchi flavor = True THEN 1.000 51 0.902 IF Yolk = True and Cumin BBQ = True THEN 1.000

Cheese Corn Flavor Rule Set

Cheese Corn Flavor = 1.000 ID Probability Rule

46 1 IF Crab flavor = True and Tomato flavor = True THEN 1.000 52 0.981 IF Crab flavor = True THEN 1.000 24 0.958 IF Crab flavor = True and BBQ flavor = True THEN 1.000 22 0.955 IF Crab flavor = True and Sauced beef flavor = True THEN 1.000 20 0.95 IF Crab flavor = True and Chicken flavor = True THEN 1.000 19 0.947 IF Crab flavor = True and Curry beef = True THEN 1.000

Tomato Flavor Rule Set

74

Tomato Flavor = 1.000 ID Probability Rule

51 0.902 IF Crab flavor = True and Cheese corn flavor = True THEN 1.000 Chicken Flavor Rule Set

Chicken Flavor = 1.000 ID Probability Rule

22 1 IF Cheese corn flavor = True and Chives flavor = True THEN 1.000

18 1 IF Tomato flavor = True and Cumin BBQ = True THEN 1.000

34 0.971 IF Chives flavor = True and Hot & spice sichuan = True THEN 1.000

41 0.951 IF Chives flavor = True and Kimchi flavor = True THEN 1.000

45 0.933 IF Chives flavor = True and Pork steak = True THEN 1.000

50 0.92 IF Chives flavor = True and BBQ flavor = True THEN 1.000

47 0.915 IF Chives flavor = True and Cumin BBQ = True THEN 1.000

57 0.912 IF Kimchi flavor = True and BBQ flavor = True THEN 1.000

54 0.907 IF Chives flavor = True THEN 1.000

43 0.907 IF Chives flavor = True and Sauced beef flavor = True THEN 1.000

21 0.905 IF Cheese corn flavor = True and Kimchi flavor = True THEN 1.000

21 0.905 IF Tomato flavor = True and Kimchi flavor = True THEN 1.000

52 0.904 IF Hot & spice sichuan = True and Cumin BBQ = True THEN 1.000

Curry Beef Flavor Rule Set

Curry Beef Flavor = 1.000 ID Probability Rule

24 1 IF Cheese corn flavor = True and Pepper beef steak = True THEN 1.000

63 0.968 IF Hot & spices salt = True and Sauced beef flavor = True THEN 1.000

27 0.963 IF Pork steak = True and Hot & spices salt = True THEN 1.000

26 0.962 IF Tomato flavor = True and Pepper beef steak = True THEN 1.000

76 0.961 IF Pepper beef steak = True and Sauced beef flavor = True THEN 1.000

50 0.96 IF Hot & spices salt = True and Cumin BBQ = True THEN 1.000

24 0.958 IF Chives flavor = True and Hot & spices salt = True THEN 1.000

44 0.955 IF Hot & spices salt = True and Chicken flavor = True THEN 1.000

21 0.952 IF Cheese corn flavor = True and Kimchi flavor = True THEN 1.000

54 0.944 IF Pepper beef steak = True and BBQ flavor = True THEN 1.000

53 0.943 IF Hot & spices salt = True and Kimchi flavor = True THEN 1.000

63 0.937 IF Kimchi flavor = True and Pepper beef steak = True THEN 1.000

61 0.934 IF Cumin BBQ = True and Pepper beef steak = True THEN 1.000

42 0.929 IF Yolk = True and Pepper beef steak = True THEN 1.000

55 0.927 IF Pepper beef steak = True and Chicken flavor = True THEN 1.000

66 0.924 IF Kimchi flavor = True and Sauced beef flavor = True THEN 1.000

37 0.919 IF Pork steak = True and Pepper beef steak = True THEN 1.000

72 0.917 IF Hot & spices salt = True and Pepper beef steak = True THEN 1.000

47 0.915 IF Hot & spices salt = True and BBQ flavor = True THEN 1.000

75

34 0.912 IF Chives flavor = True and Pepper beef steak = True THEN 1.000


92 0.902 IF Pepper beef steak = True THEN 1.000

Pepper Beef Steak Flavor Rule Set

Pepper Beef Steak Flavor = 1.000 ID Probability Rule

54 0.944 IF Pepper beef steak = True and BBQ flavor = True THEN 1.000

53 0.943 IF Hot & spices salt = True and Kimchi flavor = True THEN 1.000

63 0.937 IF Kimchi flavor = True and Pepper beef steak = True THEN 1.000

61 0.934 IF Cumin BBQ = True and Pepper beef steak = True THEN 1.000

42 0.929 IF Yolk = True and Pepper beef steak = True THEN 1.000


66 0.924 IF Kimchi flavor = True and Sauced beef flavor = True THEN 1.000

37 0.919 IF Pork steak = True and Pepper beef steak = True THEN 1.000

72 0.917 IF Hot & spices salt = True and Pepper beef steak = True THEN 1.000

47 0.915 IF Hot & spices salt = True and BBQ flavor = True THEN 1.000

34 0.912 IF Chives flavor = True and Pepper beef steak = True THEN 1.000


92 0.902 IF Pepper beef steak = True THEN PBS

Yolk Flavor Rule Set

Yolk Flavor = 1.000 ID Probability Rule

34 0.941 IF Chives flavor = True and Hot & spice sichuan = True THEN 1.000


38 0.921 IF Chives flavor = True and Curry beef = True THEN 1.000

50 0.92 IF Chives flavor = True and BBQ flavor = True THEN 1.000


22 0.909 IF Cheese corn flavor = True and Chives flavor = True THEN 1.000


BBQ Flavor Rule Set

BBQ Flavor = 1.000 ID Probability Rule


47 0.936 IF Pork steak = True and Kimchi flavor = True THEN 1.000


43 0.93 IF Chives flavor = True and Sauced beef flavor = True THEN 1.000

41 0.927 IF Chives flavor = True and Kimchi flavor = True THEN 1.000

41 0.927 IF Yolk = True and Kimchi flavor = True THEN 1.000


26 0.923 IF Cheese corn flavor = True and Yolk = True THEN 1.000

38 0.921 IF Chives flavor = True and Curry beef = True THEN 1.000

76

49 0.918 IF Hot&spice sichuan = True and Sauced beef flavor = True THEN 1.000

24 0.917 IF Chives flavor = True and Hot & spices salt = True THEN 1.000

56 0.911 IF Pork steak = True and Cumin BBQ = True THEN 1.000

22 0.909 IF Cheese corn flavor = True and Chives flavor = True THEN 1.000

52 0.904 IF Hot&spice sichuan = True and Cumin BBQ = True THEN 1.000

51 0.902 IF Yolk = True and Cumin BBQ = True THEN 1.000

Kimchi Flavor Rule Set

Kimchi Flavor = 1.000 ID Probability Rule

41 0.902 IF Pork steak = True and Curry beef = True THEN 1.000

44 0.932 IF Hot & spices salt = True and Chicken flavor = True THEN 1.000


58 0.914 IF Chicken flavor = True and Curry beef = True THEN 1.000

62 0.903 IF Chicken flavor = True and Sauced beef flavor = True THEN 1.000 Table 3 Scoring information for Top 10 Rules in All Regions Year 13 14 15 14 15 13 15 13 14 13 North South Mid East SW Jan. May Sep. Jan. May. Sep. Jan. May. Sep. Jan. TF 1 0 0 1 1 0 0 1 1 1 CF 0 0 0 1 1 0 0 1 1 0 SBF 1 1 1 0 0 1 1 1 1 0 CCF 0 0 1 1 1 0 0 1 1 0 BQF 0 0 1 0 1 1 1 0 1 0 PBS 1 1 1 0 0 1 1 0 1 0 CKF 1 0 1 0 0 1 1 0 1 0 CBF 1 1 1 0 0 1 1 1 1 0 CBQ 1 1 1 0 0 1 1 0 1 0 HSS 1 1 1 0 0 1 0 0 1 1 PSF 0 0 1 0 0 1 1 0 0 0 YF 0 0 1 0 0 1 1 0 0 0 KF 1 1 1 0 0 1 1 0 1 0 CVF 0 0 1 0 0 1 1 0 1 0 HSC 0 0 1 0 0 0 1 0 0 0

Scoring Information Prediction 1 CF PBS CBQ CCF CCF CBQ CBQ CCF CCF PBS Confidence 1 1 0.99 1 1 1 1 1 1 1 0.83 Rule ID 1 14 15 2 4 4 2 2 4 4 306

77

Prediction 2 PBS CBF CBF CCF CCF SBF SBF CCF CKF Confidence 2 0.99 0.97 1 0.98 0.98 1 1 0.98 1 Rule ID 2 15 30 12 18 18 1 1 18 14 Prediction 3 CBF PBF CKF TF CCF SBF SBF CCF CBF Confidence 3 0.99 0.96 1 0.90 0.96 1 1 0.96 1 Rule ID 3 30 35 13 139 41 7 7 52 12 Table 4 Results for the North China Region

Consequent Antecedent Support % Confidence % Lift

1 CVF YF 36.111 100 2.25 2 CF YF and HSS 11.111 100 2.118 3 TF YF and HSS 11.111 100 2.118 4 CBF YF and HSS 11.111 100 1.895 5 PBS YF and HSS 11.111 100 2 6 CVF YF and HSS 11.111 100 2.25 7 CCF YF and HSS 11.111 100 2.118 8 SBF YF and HSS 11.111 100 1.8 9 KF YF and HSS 11.111 100 1.636

10 PSF YF and CKF 30.556 100 2.571 11 CBF YF and PSF 33.333 100 2.571 12 PSF YF and CBF 33.333 100 2.571 13 CVF YF and PSF 33.333 100 2.25 14 PSF YF and HSC 30.556 100 2.571 15 BQF YF and PSF 33.333 100 2.25 16 PSF YF and BQF 33.333 100 2.571 17 CBF YF and CKF 30.556 100 2.571 18 CVF YF and CKF 30.556 100 2.25 19 BQF YF and CKF 30.556 100 2.25 20 CCF YF and CKF 30.556 100 2.118 21 CVF YF and CBF 33.333 100 2.25 22 CBF YF and HSC 30.556 100 2.571 23 BQF YF and CBF 33.333 100 2.25 24 CBF YF and BQF 33.333 100 2.571 25 CF YF and CBF 16.667 100 2.118 26 CF YF and PBS 19.444 100 2.118 27 CVF YF and CF 27.778 100 2.25 28 CVF YF and TF 22.222 100 2.25 29 CCF YF and TF 22.222 100 2.118 30 PBS YF and CBF 16.667 100 2 31 CVF YF and CBF 16.667 100 2.25 32 CCF YF and CBF 16.667 100 2.118 33 SBF YF and CBF 16.667 100 1.8

78

34 KF YF and CBF 16.667 100 1.636 35 CVF YF and PBS 19.444 100 2.25 36 CCF YF and PBS 19.444 100 2.118 37 SBF YF and PBS 19.444 100 1.8 38 KF YF and PBS 19.444 100 1.636 39 CVF YF and HSC 30.556 100 2.25 40 CVF YF and BQF 33.333 100 2.25 41 CVF YF and CCF 33.333 100 2.25 42 CVF YF and SBF 27.778 100 2.25 43 CVF YF and KF 27.778 100 2.25 44 BQF YF and HSC 30.556 100 2.25 45 CCF YF and KF 27.778 100 2.118 46 CKF HSS and PSF 11.111 100 2.571 47 CBF HSS and PSF 11.111 100 2.571 48 CF HSS and PSF 11.111 100 2.118 49 TF HSS and PSF 11.111 100 2.118 50 CBF HSS and PSF 11.111 100 1.895 51 PBS HSS and PSF 11.111 100 2 52 CVF HSS and PSF 11.111 100 2.25 53 BQF HSS and PSF 11.111 100 2.25 54 CCF HSS and PSF 11.111 100 2.118 55 SBF HSS and PSF 11.111 100 1.8 56 KF HSS and PSF 11.111 100 1.636 57 CBF HSS and CKF 16.667 100 1.895 58 PBS HSS and CKF 16.667 100 2 59 KF HSS and CKF 16.667 100 1.636 60 CF HSS and CBF 13.889 100 2.118 61 TF HSS and CBF 13.889 100 2.118 62 CBF HSS and CBF 13.889 100 1.895 63 PBS HSS and CBF 13.889 100 2 64 CCF HSS and CBF 13.889 100 2.118 65 SBF HSS and CBF 13.889 100 1.8 66 KF HSS and CBF 13.889 100 1.636 67 CBF HSS and CF 25 100 1.895 68 KF HSS and CF 25 100 1.636 69 TF HSS and BQF 19.444 100 2.118 70 CBF HSS and CVF 19.444 100 1.895 71 CBF HSS and BQF 19.444 100 1.895 72 CBF HSS and CCF 25 100 1.895 73 CBF HSS and SBF 36.111 100 1.895 74 CBF HSS and KF 44.444 100 1.895

79

75 PBS HSS and HSC 22.222 100 2 76 PBS HSS and BQF 19.444 100 2 77 PBS HSS and CCF 25 100 2 78 SBF HSS and CVF 19.444 100 1.8 79 SBF HSS and BQF 19.444 100 1.8 80 KF HSS and BQF 19.444 100 1.636 81 SBF HSS and CCF 25 100 1.8 82 KF HSS and CCF 25 100 1.636 83 CBF PSF and CKF 33.333 100 2.571 84 PSF CKF and CBF 33.333 100 2.571 85 PSF CKF and CF 25 100 2.571 86 CKF PSF and TF 22.222 100 2.571 87 CKF PSF and CBF 16.667 100 2.571 88 CKF PSF and PBS 19.444 100 2.571 89 CVF PSF and CKF 33.333 100 2.25 90 PSF CKF and CVF 33.333 100 2.571 91 BQF PSF and CKF 33.333 100 2.25 92 CCF PSF and CKF 33.333 100 2.118 93 CKF PSF and CCF 33.333 100 2.571 94 CKF PSF and KF 27.778 100 2.571 95 CBF PSF and CF 27.778 100 2.571 96 CBF PSF and TF 22.222 100 2.571 97 CBF PSF and CBF 16.667 100 2.571 98 CBF PSF and PBS 19.444 100 2.571 99 CVF PSF and CBF 36.111 100 2.25

100 CBF PSF and CVF 36.111 100 2.571 101 PSF CBF and CVF 36.111 100 2.571 102 CBF PSF and HSC 33.333 100 2.571 103 BQF PSF and CBF 36.111 100 2.25 104 CBF PSF and BQF 36.111 100 2.571 105 PSF CBF and BQF 36.111 100 2.571 106 CBF PSF and CCF 33.333 100 2.571 107 CBF PSF and SBF 27.778 100 2.571 108 CBF PSF and KF 27.778 100 2.571 109 CF PSF and CBF 16.667 100 2.118 110 CF PSF and PBS 19.444 100 2.118 111 CVF PSF and CF 27.778 100 2.25 112 BQF PSF and CF 27.778 100 2.25 113 PSF CF and BQF 27.778 100 2.571 114 CVF PSF and TF 22.222 100 2.25 115 BQF PSF and TF 22.222 100 2.25

80

116 CCF PSF and TF 22.222 100 2.118 117 PBS PSF and CBF 16.667 100 2 118 CVF PSF and CBF 16.667 100 2.25 119 BQF PSF and CBF 16.667 100 2.25 120 CCF PSF and CBF 16.667 100 2.118 121 SBF PSF and CBF 16.667 100 1.8 122 KF PSF and CBF 16.667 100 1.636 123 CVF PSF and PBS 19.444 100 2.25 124 BQF PSF and PBS 19.444 100 2.25 125 CCF PSF and PBS 19.444 100 2.118 126 SBF PSF and PBS 19.444 100 1.8 127 KF PSF and PBS 19.444 100 1.636 128 CVF PSF and HSC 33.333 100 2.25 129 PSF CVF and HSC 33.333 100 2.571 130 BQF PSF and CVF 36.111 100 2.25 131 CVF PSF and BQF 36.111 100 2.25 132 PSF CVF and BQF 36.111 100 2.571 133 CVF PSF and CCF 33.333 100 2.25 134 CVF PSF and SBF 27.778 100 2.25 135 CVF PSF and KF 27.778 100 2.25 136 BQF PSF and HSC 33.333 100 2.25 137 BQF PSF and CCF 33.333 100 2.25 138 BQF PSF and SBF 27.778 100 2.25 139 BQF PSF and KF 27.778 100 2.25 140 CCF PSF and KF 27.778 100 2.118 141 CBF CKF and CF 25 100 2.571 142 CVF CKF and CBF 33.333 100 2.25 143 CBF CKF and CVF 33.333 100 2.571 144 BQF CKF and CBF 33.333 100 2.25 145 CCF CKF and CBF 33.333 100 2.118 146 CVF CKF and CF 25 100 2.25 147 BQF CKF and CF 25 100 2.25 148 CCF CKF and CF 25 100 2.118 149 BQF CKF and TF 25 100 2.25 150 CCF CKF and TF 25 100 2.118 151 PBS CKF and CBF 22.222 100 2 152 KF CKF and CBF 22.222 100 1.636 153 KF CKF and PBS 25 100 1.636 154 BQF CKF and CVF 33.333 100 2.25 155 CCF CKF and CVF 33.333 100 2.118 156 BQF CKF and HSC 33.333 100 2.25

81

157 CCF CKF and HSC 33.333 100 2.118 158 CCF CKF and BQF 36.111 100 2.118 159 BQF CKF and CCF 36.111 100 2.25 160 BQF CKF and SBF 27.778 100 2.25 161 CCF CKF and SBF 27.778 100 2.118 162 CF CBF and CBF 19.444 100 2.118 163 CF CBF and PBS 22.222 100 2.118 164 CBF CF and BQF 27.778 100 2.571 165 CCF CBF and TF 25 100 2.118 166 PBS CBF and CBF 19.444 100 2 167 CCF CBF and CBF 19.444 100 2.118 168 SBF CBF and CBF 19.444 100 1.8 169 KF CBF and CBF 19.444 100 1.636 170 CCF CBF and PBS 22.222 100 2.118 171 SBF CBF and PBS 22.222 100 1.8 172 KF CBF and PBS 22.222 100 1.636 173 CBF CVF and HSC 33.333 100 2.571 174 BQF CBF and CVF 36.111 100 2.25 175 CVF CBF and BQF 36.111 100 2.25 176 CBF CVF and BQF 36.111 100 2.571 177 CCF CBF and KF 30.556 100 2.118 178 PBS CF and TF 25 100 2 179 KF CF and TF 25 100 1.636 180 KF CF and CBF 30.556 100 1.636 181 CF PBS and CVF 22.222 100 2.118 182 KF CF and PBS 30.556 100 1.636 183 CVF CF and BQF 27.778 100 2.25 184 KF TF and PBS 36.111 100 1.636 185 PBS CBF and HSC 25 100 2 186 PBS CBF and BQF 25 100 2 187 PBS CBF and CCF 30.556 100 2 188 KF CBF and PBS 44.444 100 1.636 189 SBF CBF and CVF 25 100 1.8 190 SBF CBF and HSC 25 100 1.8 191 KF CBF and HSC 25 100 1.636 192 SBF CBF and BQF 25 100 1.8 193 KF CBF and BQF 25 100 1.636 194 SBF CBF and CCF 30.556 100 1.8 195 KF CBF and CCF 30.556 100 1.636 196 CCF PBS and CVF 22.222 100 2.118 197 SBF PBS and CVF 22.222 100 1.8

82

198 KF PBS and CVF 22.222 100 1.636 199 SBF PBS and BQF 27.778 100 1.8 200 KF PBS and BQF 27.778 100 1.636 201 SBF PBS and CCF 33.333 100 1.8 202 KF PBS and CCF 33.333 100 1.636 203 KF PBS and SBF 38.889 100 1.636 204 BQF CVF and HSC 33.333 100 2.25 205 KF CBF 52.778 94.737 1.55 206 KF PBS 50 94.444 1.545 207 KF HSS and CBF 47.222 94.118 1.54 208 CBF PBS and KF 47.222 94.118 1.783 209 CBF HSS and PBS 41.667 93.333 1.768 210 KF HSS and PBS 41.667 93.333 1.527 211 KF CBF and SBF 41.667 93.333 1.527 212 CBF PSF 38.889 92.857 2.388 213 PSF CBF 38.889 92.857 2.388 214 CVF PSF 38.889 92.857 2.089 215 BQF PSF 38.889 92.857 2.089 216 BQF CKF 38.889 92.857 2.089 217 CCF CKF 38.889 92.857 1.966 218 CVF CBF 38.889 92.857 2.089 219 HSC CBF 38.889 92.857 1.966 220 BQF CBF 38.889 92.857 2.089 221 CCF CBF 38.889 92.857 1.966 222 CKF BQF and CCF 38.889 92.857 2.388 223 PBS TF and KF 38.889 92.857 1.857 224 CBF PBS and SBF 38.889 92.857 1.759 225 KF CCF and SBF 38.889 92.857 1.519 226 PSF YF 36.111 92.308 2.374 227 CBF YF 36.111 92.308 2.374 228 BQF YF 36.111 92.308 2.077 229 CCF YF 36.111 92.308 1.955 230 YF PSF and CBF 36.111 92.308 2.556 231 PSF YF and CVF 36.111 92.308 2.374 232 YF PSF and CVF 36.111 92.308 2.556 233 YF PSF and BQF 36.111 92.308 2.556 234 CBF YF and CVF 36.111 92.308 2.374 235 YF CBF and CVF 36.111 92.308 2.556 236 YF CBF and BQF 36.111 92.308 2.556 237 BQF YF and CVF 36.111 92.308 2.077 238 YF CVF and BQF 36.111 92.308 2.556

83

239 CCF YF and CVF 36.111 92.308 1.955 240 YF CVF and CCF 36.111 92.308 2.556 241 CBF HSS and TF 36.111 92.308 1.749 242 HSS TF and CBF 36.111 92.308 1.749 243 KF HSS and SBF 36.111 92.308 1.51 244 CKF PSF and CBF 36.111 92.308 2.374 245 CKF PSF and CVF 36.111 92.308 2.374 246 CKF PSF and BQF 36.111 92.308 2.374 247 PSF CKF and BQF 36.111 92.308 2.374 248 PSF CKF and CCF 36.111 92.308 2.374 249 HSC PSF and CBF 36.111 92.308 1.955 250 PSF CBF and HSC 36.111 92.308 2.374 251 CCF PSF and CBF 36.111 92.308 1.955 252 PSF CBF and CCF 36.111 92.308 2.374 253 HSC PSF and CVF 36.111 92.308 1.955 254 CCF PSF and CVF 36.111 92.308 1.955 255 PSF CVF and CCF 36.111 92.308 2.374 256 HSC PSF and BQF 36.111 92.308 1.955 257 CCF PSF and BQF 36.111 92.308 1.955 258 CKF CBF and CVF 36.111 92.308 2.374 259 CBF CKF and BQF 36.111 92.308 2.374 260 CKF CBF and BQF 36.111 92.308 2.374 261 CBF CKF and CCF 36.111 92.308 2.374 262 CKF CBF and CCF 36.111 92.308 2.374 263 CVF CKF and BQF 36.111 92.308 2.077 264 CKF CVF and BQF 36.111 92.308 2.374 265 CVF CKF and CCF 36.111 92.308 2.077 266 CKF CVF and CCF 36.111 92.308 2.374 267 HSC CKF and BQF 36.111 92.308 1.955 268 HSC CKF and CCF 36.111 92.308 1.955 269 HSC CBF and CVF 36.111 92.308 1.955 270 CVF CBF and HSC 36.111 92.308 2.077 271 CCF CBF and CVF 36.111 92.308 1.955 272 CVF CBF and CCF 36.111 92.308 2.077 273 CBF CVF and CCF 36.111 92.308 2.374 274 BQF CBF and HSC 36.111 92.308 2.077 275 HSC CBF and BQF 36.111 92.308 1.955 276 CCF CBF and HSC 36.111 92.308 1.955 277 HSC CBF and CCF 36.111 92.308 1.955 278 CCF CBF and BQF 36.111 92.308 1.955 279 BQF CBF and CCF 36.111 92.308 2.077

84

280 PBS TF and CBF 36.111 92.308 1.846 281 CBF TF and PBS 36.111 92.308 1.749 282 KF TF and CBF 36.111 92.308 1.51 283 HSC CVF and BQF 36.111 92.308 1.955 284 CCF CVF and BQF 36.111 92.308 1.955 285 BQF CVF and CCF 36.111 92.308 2.077 286 CCF HSC and KF 36.111 92.308 1.955 287 CCF BQF and KF 36.111 92.308 1.955 288 CKF YF and PSF 33.333 91.667 2.357 289 YF PSF and CKF 33.333 91.667 2.538 290 HSC YF and PSF 33.333 91.667 1.941 291 YF PSF and HSC 33.333 91.667 2.538 292 CCF YF and PSF 33.333 91.667 1.941 293 PSF YF and CCF 33.333 91.667 2.357 294 YF PSF and CCF 33.333 91.667 2.538 295 CKF YF and CBF 33.333 91.667 2.357 296 YF CKF and CBF 33.333 91.667 2.538 297 YF CKF and CVF 33.333 91.667 2.538 298 CKF YF and BQF 33.333 91.667 2.357 299 CKF YF and CCF 33.333 91.667 2.357 300 HSC YF and CBF 33.333 91.667 1.941 301 CCF YF and CBF 33.333 91.667 1.941 302 CBF YF and CCF 33.333 91.667 2.357 303 YF CVF and HSC 33.333 91.667 2.538 304 HSC YF and BQF 33.333 91.667 1.941 305 CCF YF and BQF 33.333 91.667 1.941 306 BQF YF and CCF 33.333 91.667 2.062 307 HSC PSF and CKF 33.333 91.667 1.941 308 CKF PSF and HSC 33.333 91.667 2.357 309 PSF CKF and HSC 33.333 91.667 2.357 310 CCF PSF and HSC 33.333 91.667 1.941 311 HSC PSF and CCF 33.333 91.667 1.941 312 HSC CKF and CBF 33.333 91.667 1.941 313 CBF CKF and HSC 33.333 91.667 2.357 314 HSC CKF and CVF 33.333 91.667 1.941 315 CVF CKF and HSC 33.333 91.667 2.062 316 CKF CVF and HSC 33.333 91.667 2.357 317 BQF CKF and KF 33.333 91.667 2.062 318 CCF CKF and KF 33.333 91.667 1.941 319 KF CF and CCF 33.333 91.667 1.5 320 KF CF and SBF 33.333 91.667 1.5

85

321 SBF TF and CCF 33.333 91.667 1.65 322 KF TF and CCF 33.333 91.667 1.5 323 CBF PBS and CCF 33.333 91.667 1.737 324 CCF CVF and HSC 33.333 91.667 1.941 325 CCF CVF and KF 33.333 91.667 1.941 326 HSC YF and CKF 30.556 90.909 1.925 327 CKF YF and HSC 30.556 90.909 2.338 328 CCF YF and HSC 30.556 90.909 1.925 329 PSF CBF and CF 30.556 90.909 2.338 330 PSF CBF and SBF 30.556 90.909 2.338 331 PSF CBF and KF 30.556 90.909 2.338 332 CKF CBF and KF 30.556 90.909 2.338 333 CVF CBF and CF 30.556 90.909 2.045 334 HSC CBF and CF 30.556 90.909 1.925 335 CBF CF and HSC 30.556 90.909 2.338 336 BQF CBF and CF 30.556 90.909 2.045 337 CCF CBF and CF 30.556 90.909 1.925 338 CVF CBF and SBF 30.556 90.909 2.045 339 CVF CBF and KF 30.556 90.909 2.045 340 HSC CBF and SBF 30.556 90.909 1.925 341 HSC CBF and KF 30.556 90.909 1.925 342 BQF CBF and SBF 30.556 90.909 2.045 343 BQF CBF and KF 30.556 90.909 2.045 344 CCF CBF and SBF 30.556 90.909 1.925 345 PBS CF and CBF 30.556 90.909 1.818 346 CBF CF and PBS 30.556 90.909 1.722 347 SBF CF and CBF 30.556 90.909 1.636 348 CCF CF and PBS 30.556 90.909 1.925 349 SBF CF and PBS 30.556 90.909 1.636 350 CCF CF and HSC 30.556 90.909 1.925 351 CCF TF and BQF 30.556 90.909 1.925 352 SBF TF and BQF 30.556 90.909 1.636 353 KF TF and BQF 30.556 90.909 1.488 354 SBF PBS and HSC 30.556 90.909 1.636 355 KF PBS and HSC 30.556 90.909 1.488 356 PSF YF and CF 27.778 90 2.314 357 YF PSF and CF 27.778 90 2.492 358 PSF YF and SBF 27.778 90 2.314 359 YF PSF and SBF 27.778 90 2.492 360 PSF YF and KF 27.778 90 2.314 361 YF PSF and KF 27.778 90 2.492

86

362 CKF YF and KF 27.778 90 2.314 363 CBF YF and CF 27.778 90 2.314 364 CBF YF and SBF 27.778 90 2.314 365 CBF YF and KF 27.778 90 2.314 366 BQF YF and CF 27.778 90 2.025 367 YF CF and BQF 27.778 90 2.492 368 CCF YF and CF 27.778 90 1.906 369 BQF YF and SBF 27.778 90 2.025 370 BQF YF and KF 27.778 90 2.025 371 CCF YF and SBF 27.778 90 1.906 372 CKF PSF and CF 27.778 90 2.314 373 CKF PSF and SBF 27.778 90 2.314 374 PSF CKF and SBF 27.778 90 2.314 375 HSC PSF and CF 27.778 90 1.906 376 CCF PSF and CF 27.778 90 1.906 377 HSC PSF and SBF 27.778 90 1.906 378 HSC PSF and KF 27.778 90 1.906 379 CCF PSF and SBF 27.778 90 1.906 380 CBF CKF and SBF 27.778 90 2.314 381 CKF CF and BQF 27.778 90 2.314 382 CVF CKF and SBF 27.778 90 2.025 383 HSC CKF and SBF 27.778 90 1.906 384 KF CKF and SBF 27.778 90 1.473 385 HSC CF and BQF 27.778 90 1.906 386 CCF CF and BQF 27.778 90 1.906 387 TF PBS and BQF 27.778 90 1.906 388 CCF TF and CVF 27.778 90 1.906 389 SBF TF and CVF 27.778 90 1.62 390 BQF TF and HSC 27.778 90 2.025 391 CCF TF and HSC 27.778 90 1.906 392 SBF TF and HSC 27.778 90 1.62 393 KF TF and HSC 27.778 90 1.473 394 CBF PBS and BQF 27.778 90 1.705 395 CCF PBS and BQF 27.778 90 1.906 Table 5 Scoring for All Rule Sets in the North China


Cheese Corn Flavor = 1.000

ID Probability Rule

10 1 IF Yolk = True and Crab flavor = True then 1.000

10 1 IF Yolk = True and Sauced beef flavor = True then 1.000

10 1 IF Pork steak = True and Crab flavor = True then 1.000

87

10 1 IF Pork steak = True and Sauced beef flavor = True then 1.000

10 1 IF Crab flavor = True and BBQ flavor = True then 1.000

10 1 IF Tomato flavor = True and Chives flavor = True then 1.000

10 1 IF Tomato flavor = True and Hot sichuan = True then 1.000

10 1 IF Pepper beef steak = True and BBQ flavor = True then 1.000

11 1 IF Yolk = True and Hot sichuan = True then 1.000

11 1 IF Cumin BBQ = True and Crab flavor = True then 1.000

11 1 IF Cumin BBQ = True and Sauced beef flavor = True then 1.000

11 1 IF Crab flavor = True and Pepper beef steak = True then 1.000

11 1 IF Crab flavor = True and Hot sichuan = True then 1.000

11 1 IF Tomato flavor = True and BBQ flavor = True then 1.000

12 1 IF Yolk = True and Pork steak = True then 1.000

12 1 IF Yolk = True and Cumin BBQ = True then 1.000

12 1 IF Yolk = True and BBQ flavor = True then 1.000

12 1 IF Pork steak = True and Hot sichuan = True then 1.000

12 1 IF Chicken flavor = True and Kimchi flavor = True then 1.000

12 1 IF Chives flavor = True and Hot sichuan = True then 1.000

12 1 IF Chives flavor = True and Kimchi flavor = True then 1.000

13 1 IF Yolk = True then 1.000

13 1 IF Yolk = True and Chives flavor = True then 1.000

13 1 IF Pork steak = True and Cumin BBQ = True then 1.000

13 1 IF Pork steak = True and Chives flavor = True then 1.000

13 0.929 IF Pork steak = True and BBQ flavor = True then 1.000

13 0.929 IF Cumin BBQ = True and Chives flavor = True then 1.000

13 0.923 IF Cumin BBQ = True and Hot sichuan = True then 1.000

13 0.923 IF Cumin BBQ = True and BBQ flavor = True then 1.000

13 0.923 IF Chives flavor = True and BBQ flavor = True then 1.000

13 0.923 IF Hot sichuan = True and Kimchi flavor = True then 1.000

13 0.923 IF BBQ flavor = True and Kimchi flavor = True then 1.000

14 0.923 IF Chicken flavor = True then 1.000

14 0.923 IF Cumin BBQ = True then 1.000

4 0.923 IF Yolk = True and spices salt = True then 1.000

11 0.923 IF Yolk = True and Chicken flavor = True then 1.000

8 0.923 IF Yolk = True and Tomato flavor = True then 1.000

6 0.923 IF Yolk = True and Curry beef = True then 1.000

7 0.917 IF Yolk = True and Pepper beef steak = True then 1.000

10 0.917 IF Yolk = True and Kimchi flavor = True then 1.000

4 0.917 IF spices salt = True and Pork steak = True then 1.000

5 0.917 IF spices salt = True and Cumin BBQ = True then 1.000

12 0.917 IF Pork steak = True and Chicken flavor = True then 1.000

8 0.917 IF Pork steak = True and Tomato flavor = True then 1.000

88

6 0.917 IF Pork steak = True and Curry beef = True then 1.000

7 0.909 IF Pork steak = True and Pepper beef steak = True then 1.000

10 0.909 IF Pork steak = True and Kimchi flavor = True then 1.000

12 0.909 IF Chicken flavor = True and Cumin BBQ = True then 1.000

9 0.909 IF Chicken flavor = True and Crab flavor = True then 1.000

9 0.909 IF Chicken flavor = True and Tomato flavor = True then 1.000

12 0.909 IF Chicken flavor = True and Chives flavor = True then 1.000

12 0.9 IF Chicken flavor = True and Hot sichuan = True then 1.000

13 0.9 IF Chicken flavor = True and BBQ flavor = True then 1.000

10 0.9 IF Chicken flavor = True and Sauced beef flavor = True then 1.000

9 0.9 IF Cumin BBQ = True and Tomato flavor = True then 1.000

7 0.9 IF Cumin BBQ = True and Curry beef = True then 1.000

8 0.9 IF Cumin BBQ = True and Pepper beef steak = True then 1.000

11 0.9 IF Cumin BBQ = True and Kimchi flavor = True then 1.000

8 0.9 IF Pepper beef steak = True and Chives flavor = True then 1.000


Kimchi Flavor = 1.000

ID Probability Rule

4 1 IF Yolk = True and spices salt = True then 1.000

6 1 IF Yolk = True and Curry beef = True then 1.000

7 1 IF Yolk = True and Pepper beef steak = True then 1.000

4 1 IF spices salt = True and Pork steak = True then 1.000

6 1 IF spices salt = True and Chicken flavor = True then 1.000

5 1 IF spices salt = True and Cumin BBQ = True then 1.000

9 1 IF spices salt = True and Crab flavor = True then 1.000

7 1 IF spices salt = True and BBQ flavor = True then 1.000

9 1 IF spices salt = True and Cheese corn flavor = True then 1.000

6 1 IF Pork steak = True and Curry beef = True then 1.000

7 1 IF Pork steak = True and Pepper beef steak = True then 1.000

8 1 IF Chicken flavor = True and Curry beef = True then 1.000

9 1 IF Chicken flavor = True and Pepper beef steak = True then 1.000

7 1 IF Cumin BBQ = True and Curry beef = True then 1.000

8 1 IF Cumin BBQ = True and Pepper beef steak = True then 1.000

9 1 IF Crab flavor = True and Tomato flavor = True then 1.000

11 1 IF Crab flavor = True and Curry beef = True then 1.000

11 1 IF Crab flavor = True and Pepper beef steak = True then 1.000

13 1 IF Tomato flavor = True and Pepper beef steak = True then 1.000

16 1 IF Curry beef = True and Pepper beef steak = True then 1.000

9 1 IF Curry beef = True and Hot sichuan = True then 1.000

9 1 IF Curry beef = True and BBQ flavor = True then 1.000

11 1 IF Curry beef = True and Cheese corn flavor = True then 1.000

89

8 1 IF Pepper beef steak = True and Chives flavor = True then 1.000


12 1 IF Pepper beef steak = True and Cheese corn flavor = True then 1.000

14 1 IF Pepper beef steak = True and Sauced beef flavor = True then 1.000

19 0.947 IF Curry beef = True then 1.000

18 0.944 IF Pepper beef steak = True then 1.000

17 0.941 IF spices salt = True and Curry beef = True then 1.000

15 0.933 IF spices salt = True and Pepper beef steak = True then 1.000

15 0.933 IF Curry beef = True and Sauced beef flavor = True then 1.000

14 0.929 IF Cheese corn flavor = True and Sauced beef flavor = True then 1.000

13 0.923 IF spices salt = True and Sauced beef flavor = True then 1.000

13 0.923 IF Tomato flavor = True and Curry beef = True then 1.000

12 0.917 IF Crab flavor = True and Cheese corn flavor = True then 1.000

12 0.917 IF Crab flavor = True and Sauced beef flavor = True then 1.000

12 0.917 IF Tomato flavor = True and Cheese corn flavor = True then 1.000

11 0.909 IF Tomato flavor = True and BBQ flavor = True then 1.000

11 0.909 IF Pepper beef steak = True and Hot sichuan = True then 1.000


10 0.9 IF Tomato flavor = True and Hot sichuan = True then 1.000

BBQ Flavor Rule Set

BBQ Flavor = 1.000

ID Probability Rule


11 1 IF Yolk = True and Chicken flavor = True then 1.000




12 1 IF Pork steak = True and Chicken flavor = True then 1.000



8 1 IF Pork steak = True and Tomato flavor = True then 1.000





12 1 IF Pork steak = True and Cheese corn flavor = True then 1.000


10 1 IF Pork steak = True and Kimchi flavor = True then 1.000

12 1 IF Chicken flavor = True and Cumin BBQ = True then 1.000

9 1 IF Chicken flavor = True and Crab flavor = True then 1.000

9 1 IF Chicken flavor = True and Tomato flavor = True then 1.000

90

12 1 IF Chicken flavor = True and Chives flavor = True then 1.000

12 1 IF Chicken flavor = True and Hot sichuan = True then 1.000

13 1 IF Chicken flavor = True and Cheese corn flavor = True then 1.000

10 1 IF Chicken flavor = True and Sauced beef flavor = True then 1.000

13 1 IF Cumin BBQ = True and Chives flavor = True then 1.000


14 0.929 IF Pork steak = True then 1.000

14 0.929 IF Chicken flavor = True then 1.000


13 0.923 IF Yolk = True then 1.000

13 0.923 IF Yolk = True and Chives flavor = True then 1.000


13 0.923 IF Cumin BBQ = True and Cheese corn flavor = True then 1.000

13 0.923 IF Chives flavor = True and Cheese corn flavor = True then 1.000

12 0.917 IF Yolk = True and Cheese corn flavor = True then 1.000

12 0.917 IF Chicken flavor = True and Kimchi flavor = True then 1.000

11 0.909 IF Cumin BBQ = True and Crab flavor = True then 1.000

11 0.909 IF Cumin BBQ = True and Sauced beef flavor = True then 1.000


10 0.9 IF Yolk = True and Crab flavor = True then 1.000

10 0.9 IF Yolk = True and Sauced beef flavor = True then 1.000



Chives Flavor Rule Set

Chives Flavor = 1.000

ID Probability Rule

10 1 IF Chicken flavor = True and Sauced beef flavor = True then 1.000

11 1 IF Cumin BBQ = True and Crab flavor = True then 1.000

11 1 IF Cumin BBQ = True and Sauced beef flavor = True then 1.000

11 1 IF Cumin BBQ = True and Kimchi flavor = True then 1.000

12 1 IF Chicken flavor = True and Hot sichuan = True then 1.000

13 1 IF Chicken flavor = True and BBQ flavor = True then 1.000

13 1 IF Chicken flavor = True and Cheese corn flavor = True then 1.000

13 1 IF Cumin BBQ = True and Hot sichuan = True then 1.000

13 1 IF Cumin BBQ = True and Cheese corn flavor = True then 1.000

14 1 IF Pork steak = True then 1.000

14 1 IF Cumin BBQ = True then 1.000

13 1 IF Yolk = True then 1.000




91


10 1 IF Yolk = True and Crab flavor = True then 1.000

8 1 IF Yolk = True and Tomato flavor = True then 1.000





12 1 IF Yolk = True and Cheese corn flavor = True then 1.000

10 1 IF Yolk = True and Sauced beef flavor = True then 1.000

10 1 IF Yolk = True and Kimchi flavor = True then 1.000







7 0.929 IF Pork steak = True and Pepper beef steak = True then 1.000

12 0.923 IF Pork steak = True and Hot sichuan = True then 1.000


12 0.923 IF Pork steak = True and Cheese corn flavor = True then 1.000

10 0.923 IF Pork steak = True and Sauced beef flavor = True then 1.000



9 0.909 IF Chicken flavor = True and Crab flavor = True then 1.000


10 0.9 IF Crab flavor = True and BBQ flavor = True then 1.000


Cumin BBQ Flavor = 1.000

ID Probability Rule













92

13 1 IF Pork steak = True and BBQ flavor = True then 1.000

12 1 IF Pork steak = True and Cheese corn flavor = True then 1.000


10 1 IF Pork steak = True and Kimchi flavor = True then 1.000




12 1 IF Chives flavor = True and Hot sichuan = True then 1.000BQ

13 1 IF Chives flavor = True and BBQ flavor = True then 1.000

14 0.929 IF Pork steak = True then 1.000




13 0.923 IF Chicken flavor = True and Cheese corn flavor = True then 1.000




11 0.909 IF Crab flavor = True and Hot sichuan = True then 1.000






Pork Steak Flavor = 1.000

ID Probability Rule





12 1 IF Chicken flavor = True and Cumin BBQ = True then 1.000



13 1 IF Cumin BBQ = True and Chives flavor = True then 1.000

13 1 IF Cumin BBQ = True and BBQ flavor = True then 1.000



13 1 IF Chives flavor = True and BBQ flavor = True then 1.000





93















Sauced Beef Flavor = 1.000

ID Probability Rule






7 1 IF spices salt = True and Chives flavor = True then 1.000







9 1 IF Curry beef = True and Chives flavor = True then 1.000






12 1 IF Pepper beef steak = True and Cheese corn flavor = True then 1.000

12 0.917 IF Tomato flavor = True and Cheese corn flavor = True then 1.000

11 0.909 IF Crab flavor = True and Curry beef = True then 1.000

11 0.909 IF Crab flavor = True and Pepper beef steak = True then 1.000

11 0.909 IF Tomato flavor = True and BBQ flavor = True then 1.000

11 0.909 IF Pepper beef steak = True and Hot sichuan = True then 1.000

10 0.9 IF Tomato flavor = True and Chives flavor = True then 1.000

94


Hot & Spice Sichuan Flavor Rule Set

Hot & Spice Sichuan Flavor = 1.000

ID Probability Rule


13 0.923 IF Pork steak = True and Cumin BBQ = True then 1.000

13 0.923 IF Pork steak = True and Chives flavor = True then 1.000








12 0.917 IF Yolk = True and Pork steak = True then 1.000

12 0.917 IF Yolk = True and Cumin BBQ = True then 1.000

12 0.917 IF Yolk = True and BBQ flavor = True then 1.000





11 0.909 IF Yolk = True and Chicken flavor = True then 1.000




10 0.9 IF Pork steak = True and Crab flavor = True then 1.000






Curry Beef Flavor = 1.000

ID Probability Rule





9 1 IF spices salt = True and Crab flavor = True then 1.000

7 1 IF spices salt = True and Chives flavor = True then 1.000



95

13 1 IF spices salt = True and Sauced beef flavor = True then 1.000

16 1 IF spices salt = True and Kimchi flavor = True then 1.000

17 0.941 IF Pepper beef steak = True and Kimchi flavor = True then 1.000

15 0.933 IF spices salt = True and Pepper beef steak = True then 1.000

14 0.929 IF Pepper beef steak = True and Sauced beef flavor = True then 1.000

13 0.923 IF spices salt = True and Tomato flavor = True then 1.000

13 0.923 IF Tomato flavor = True and Pepper beef steak = True then 1.000

12 0.917 IF Pepper beef steak = True and Cheese corn flavor = True then 1.000

11 0.909 IF Crab flavor = True and Pepper beef steak = True then 1.000

10 0.9 IF Pepper beef steak = True and BBQ flavor = True then 1.000


Pepper Beef Steak Flavor = 1.000

ID Probability Rule






8 1 IF spices salt = True and Hot sichuan = True then 1.000




8 1 IF Chicken flavor = True and Curry beef = True then 1.000


9 1 IF Crab flavor = True and Tomato flavor = True then 1.000




14 0.929 IF Tomato flavor = True and Kimchi flavor = True then 1.000


11 0.909 IF Crab flavor = True and Curry beef = True then 1.000


Yolk Flavor = 1.000

ID Probability Rule








96






12 0.917 IF Chives flavor = True and Hot sichuan = True then 1.000

10 0.9 IF Pork steak = True and Crab flavor = True then 1.000




Crab Flavor Rule Set

Crab Flavor = 1.000

ID Probability Rule












Tomato Flavor = 1.000

ID Probability Rule





10 0.9 IF Pepper beef steak = True and BBQ flavor = True then 1.000

Hot & Spice Salt Flavor Rule Set

Hot & Spice Salt Flavor = 1.000

ID Probability Rule


Chicken Flavor Rule Set

Chicken Flavor = 1.000

ID Probability Rule

10 1 IF Yolk = True and Kimchi flavor = True then 1.000



97



11 1 IF Cumin BBQ = True and Kimchi flavor = True then 1.000lavor

12 0.929 IF Yolk = True and Pork steak = True then 1.000

12 0.923 IF Yolk = True and Cumin BBQ = True then 1.000

12 0.923 IF Yolk = True and BBQ flavor = True then 1.000



12 0.923 IF Chives flavor = True and Hot sichuan = True then 1.000









14 0.917 IF BBQ flavor = True and Cheese corn flavor = True then 1.000

4 0.909 IF spices salt = True and Pork steak = True then 1.000

8 0.909 IF Pork steak = True and Tomato flavor = True then 1.000


7 0.9 IF Pork steak = True and Pepper beef steak = True then Chicken Flavor

12 0.9 IF Pork steak = True and Cheese corn flavor = True then Chicken Flavor

10 0.9 IF Pork steak = True and Kimchi flavor = True then Chicken Flavor Table 6 Scoring information for Top 10 Rules in the North China Region Jan. May Sep. Feb. Jun. Nov. Mar

. Aug. Dec. May

TF 1 1 1 0 0 0 1 1 1 0 CF 1 0 1 0 0 1 0 0 1 0 SBF 0 0 1 0 1 1 0 1 1 0 CCF 0 0 1 0 1 0 1 1 1 0 BQF 0 0 1 0 1 0 1 1 0 0 PBS 1 0 1 0 0 0 0 1 1 0 CKF 0 0 1 0 1 0 1 0 0 0 CBF 1 0 1 0 0 1 0 1 1 0 CBQ 0 0 1 0 1 0 1 0 1 0 HSS 1 1 1 0 0 1 0 1 1 0 PSF 0 0 1 0 1 0 1 0 0 0 YF 0 0 1 0 1 0 1 0 0 0 KF 1 0 1 0 1 1 1 1 1 0 CVF 0 0 1 0 1 1 1 0 0 0

98

HSC 0 0 1 0 1 0 1 0 1 0 Scoring Information

Prediction 1 PBS CBF CVF CVF KF CVF KF PBS Confidence 1 1 0.92 1 1 1 1 1 1 Rule ID 1 94 234 4 4 58 4 5 94 Prediction 2 KF PBS CCF CBF CCF CBF KF Confidence 2 1 1 1 1 1 1 1 Rule ID 2 95 94 79 104 96 6 95 Prediction 3 CBF KF BQF SBF BQF SBF CCF Confidence 3 1 1 1 1 1 1 1 Rule ID 3 104 95 80 114 97 21 98 Table 7 Results for the South China Region

ID Consequent Antecedent Support % Confidence % Lift

1 YF PSF 30.556 100 2.769

2 CBQ PSF 30.556 100 2.769

3 CKF PSF 30.556 100 2.571

4 HSC PSF 30.556 100 2

5 CVF PSF 30.556 100 2.118

6 BQF PSF 30.556 100 2

7 CKF CBQ 36.111 100 2.571

8 CVF CBQ 36.111 100 2.118

9 BQF CBQ 36.111 100 2

10 YF PSF and CF 11.111 100 2.769

11 CBQ PSF and YF 30.556 100 2.769

12 YF PSF and CBQ 30.556 100 2.769

13 CKF PSF and YF 30.556 100 2.571

14 YF PSF and CKF 30.556 100 2.769

15 YF PSF and TF 16.667 100 2.769

16 HSC PSF and YF 30.556 100 2

17 YF PSF and HSC 30.556 100 2.769

18 PSF YF and HSC 30.556 100 3.273

19 CVF PSF and YF 30.556 100 2.118

20 YF PSF and CVF 30.556 100 2.769

21 BQF PSF and YF 30.556 100 2

22 YF PSF and BQF 30.556 100 2.769

23 YF PSF and SBF 27.778 100 2.769

24 YF PSF and CCF 25 100 2.769

25 YF PSF and KF 25 100 2.769

26 CBQ PSF and CF 11.111 100 2.769

27 CKF PSF and CF 11.111 100 2.571

99

28 HSC PSF and CF 11.111 100 2

29 CVF PSF and CF 11.111 100 2.118

30 BQF PSF and CF 11.111 100 2

31 CCF PSF and CF 11.111 100 1.895

32 KF PSF and CF 11.111 100 1.8

33 CKF PSF and CBQ 30.556 100 2.571

34 CBQ PSF and CKF 30.556 100 2.769

35 CBQ PSF and TF 16.667 100 2.769

36 HSC PSF and CBQ 30.556 100 2

37 CBQ PSF and HSC 30.556 100 2.769

38 CVF PSF and CBQ 30.556 100 2.118

39 CBQ PSF and CVF 30.556 100 2.769

40 BQF PSF and CBQ 30.556 100 2

41 CBQ PSF and BQF 30.556 100 2.769

42 CBQ PSF and SBF 27.778 100 2.769

43 CBQ PSF and CCF 25 100 2.769

44 CBQ PSF and KF 25 100 2.769

45 CKF PSF and TF 16.667 100 2.571

46 HSC PSF and CKF 30.556 100 2

47 CKF PSF and HSC 30.556 100 2.571

48 CVF PSF and CKF 30.556 100 2.118

49 CKF PSF and CVF 30.556 100 2.571

50 BQF PSF and CKF 30.556 100 2

51 CKF PSF and BQF 30.556 100 2.571

52 CKF PSF and SBF 27.778 100 2.571

53 CKF PSF and CCF 25 100 2.571

54 CKF PSF and KF 25 100 2.571

55 HSC PSF and TF 16.667 100 2

56 CVF PSF and TF 16.667 100 2.118

57 BQF PSF and TF 16.667 100 2

58 SBF PSF and TF 16.667 100 1.8

59 CCF PSF and TF 16.667 100 1.895

60 CVF PSF and HSC 30.556 100 2.118

61 HSC PSF and CVF 30.556 100 2

62 BQF PSF and HSC 30.556 100 2

63 HSC PSF and BQF 30.556 100 2

64 HSC PSF and SBF 27.778 100 2

65 HSC PSF and CCF 25 100 2

66 HSC PSF and KF 25 100 2

67 BQF PSF and CVF 30.556 100 2

68 CVF PSF and BQF 30.556 100 2.118

100

69 CVF PSF and SBF 27.778 100 2.118

70 CVF PSF and CCF 25 100 2.118

71 CVF PSF and KF 25 100 2.118

72 BQF PSF and SBF 27.778 100 2

73 BQF PSF and CCF 25 100 2

74 BQF PSF and KF 25 100 2

75 CBQ YF and CF 13.889 100 2.769

76 CKF YF and CF 13.889 100 2.571

77 CVF YF and CF 13.889 100 2.118

78 BQF YF and CF 13.889 100 2

79 CCF YF and CF 13.889 100 1.895

80 KF YF and CF 13.889 100 1.8

81 CKF YF and CBQ 33.333 100 2.571

82 CBQ YF and CKF 33.333 100 2.769

83 CBQ YF and TF 19.444 100 2.769

84 CBQ YF and HSC 30.556 100 2.769

85 CVF YF and CBQ 33.333 100 2.118

86 CBQ YF and CVF 33.333 100 2.769

87 BQF YF and CBQ 33.333 100 2

88 CBQ YF and BQF 33.333 100 2.769

89 CBQ YF and SBF 30.556 100 2.769

90 CBQ YF and CCF 27.778 100 2.769

91 CBQ YF and KF 27.778 100 2.769

92 CKF YF and TF 19.444 100 2.571

93 CKF YF and HSC 30.556 100 2.571

94 CVF YF and CKF 33.333 100 2.118

95 CKF YF and CVF 33.333 100 2.571

96 BQF YF and CKF 33.333 100 2

97 CKF YF and BQF 33.333 100 2.571

98 CKF YF and SBF 30.556 100 2.571

99 CKF YF and CCF 27.778 100 2.571

100 CKF YF and KF 27.778 100 2.571

101 CVF YF and TF 19.444 100 2.118

102 BQF YF and TF 19.444 100 2

103 SBF YF and TF 19.444 100 1.8

104 CCF YF and TF 19.444 100 1.895

105 CVF YF and HSC 30.556 100 2.118

106 BQF YF and HSC 30.556 100 2

107 BQF YF and CVF 33.333 100 2

108 CVF YF and BQF 33.333 100 2.118

109 CVF YF and SBF 30.556 100 2.118

101

110 CVF YF and CCF 27.778 100 2.118

111 CVF YF and KF 27.778 100 2.118

112 BQF YF and SBF 30.556 100 2

113 BQF YF and CCF 27.778 100 2

114 BQF YF and KF 27.778 100 2

115 HSS CBF and PBS 41.667 100 1.8

116 HSS CBF and CKF 11.111 100 1.8

117 CBF HSS and CKF 11.111 100 1.895

118 HSS CBF and HSC 22.222 100 1.8

119 CBF HSS and HSC 22.222 100 1.895

120 HSS CBF and CVF 19.444 100 1.8

121 CBF HSS and CVF 19.444 100 1.895

122 HSS CBF and BQF 22.222 100 1.8

123 CBF HSS and BQF 22.222 100 1.895

124 HSS CBF and CCF 30.556 100 1.8

125 CBF HSS and CCF 30.556 100 1.895

126 HSS CBF and KF 33.333 100 1.8

127 CBF HSS and KF 33.333 100 1.895

128 CF CBF and CKF 11.111 100 2.118

129 CF CBF and CVF 19.444 100 2.118

130 PBS CBF and CKF 11.111 100 2

131 PBS CBF and CVF 19.444 100 2

132 PBS CBF and BQF 22.222 100 2

133 PBS CBF and CCF 30.556 100 2

134 PBS CBF and KF 33.333 100 2

135 TF CBF and CKF 11.111 100 2

136 SBF CBF and CKF 11.111 100 1.8

137 CCF CBF and CKF 11.111 100 1.895

138 KF CBF and CKF 11.111 100 1.8

139 TF CBF and CVF 19.444 100 2

140 KF CBF and CVF 19.444 100 1.8

141 CCF CBF and BQF 22.222 100 1.895

142 KF CBF and BQF 22.222 100 1.8

143 KF CBF and CCF 30.556 100 1.8

144 CF HSS and CKF 11.111 100 2.118

145 CF HSS and CVF 19.444 100 2.118

146 PBS HSS and CKF 11.111 100 2

147 PBS HSS and TF 33.333 100 2

148 PBS HSS and CVF 19.444 100 2

149 PBS HSS and BQF 22.222 100 2

150 PBS HSS and CCF 30.556 100 2

102

151 PBS HSS and KF 33.333 100 2

152 TF HSS and CKF 11.111 100 2

153 SBF HSS and CKF 11.111 100 1.8

154 CCF HSS and CKF 11.111 100 1.895

155 KF HSS and CKF 11.111 100 1.8

156 TF HSS and CVF 19.444 100 2

157 KF HSS and CVF 19.444 100 1.8

158 CCF HSS and BQF 22.222 100 1.895

159 KF HSS and BQF 22.222 100 1.8

160 KF HSS and CCF 30.556 100 1.8

161 CF CBQ and PBS 11.111 100 2.118

162 CKF CF and CBQ 16.667 100 2.571

163 CVF CF and CBQ 16.667 100 2.118

164 BQF CF and CBQ 16.667 100 2

165 CCF CF and CBQ 16.667 100 1.895

166 KF CF and CBQ 16.667 100 1.8

167 CF PBS and CKF 13.889 100 2.118

168 CF PBS and CVF 22.222 100 2.118

169 CCF CF and CKF 19.444 100 1.895

170 KF CF and CKF 19.444 100 1.8

171 CCF CF and HSC 19.444 100 1.895

172 KF CF and HSC 19.444 100 1.8

173 KF CF and CVF 27.778 100 1.8

174 CCF CF and BQF 25 100 1.895

175 KF CF and BQF 25 100 1.8

176 KF CF and CCF 30.556 100 1.8

177 CKF CBQ and PBS 11.111 100 2.571

178 TF CBQ and PBS 11.111 100 2

179 CVF CBQ and PBS 11.111 100 2.118

180 BQF CBQ and PBS 11.111 100 2

181 SBF CBQ and PBS 11.111 100 1.8

182 CCF CBQ and PBS 11.111 100 1.895

183 KF CBQ and PBS 11.111 100 1.8

184 CKF CBQ and TF 22.222 100 2.571

185 CKF CBQ and HSC 33.333 100 2.571

186 CVF CBQ and CKF 36.111 100 2.118

187 CKF CBQ and CVF 36.111 100 2.571

188 CBQ CKF and CVF 36.111 100 2.769

189 BQF CBQ and CKF 36.111 100 2

190 CKF CBQ and BQF 36.111 100 2.571

191 CBQ CKF and BQF 36.111 100 2.769

103

192 CKF CBQ and SBF 33.333 100 2.571

193 CKF CBQ and CCF 30.556 100 2.571

194 CKF CBQ and KF 30.556 100 2.571

195 CVF CBQ and TF 22.222 100 2.118

196 BQF CBQ and TF 22.222 100 2

197 SBF CBQ and TF 22.222 100 1.8

198 CCF CBQ and TF 22.222 100 1.895

199 CVF CBQ and HSC 33.333 100 2.118

200 BQF CBQ and HSC 33.333 100 2

201 BQF CBQ and CVF 36.111 100 2

202 CVF CBQ and BQF 36.111 100 2.118

203 CVF CBQ and SBF 33.333 100 2.118

204 CVF CBQ and CCF 30.556 100 2.118

205 CVF CBQ and KF 30.556 100 2.118

206 BQF CBQ and SBF 33.333 100 2

207 BQF CBQ and CCF 30.556 100 2

208 BQF CBQ and KF 30.556 100 2

209 TF PBS and CKF 13.889 100 2

210 SBF PBS and CKF 13.889 100 1.8

211 CCF PBS and CKF 13.889 100 1.895

212 KF PBS and CKF 13.889 100 1.8

213 TF PBS and CVF 22.222 100 2

214 TF PBS and SBF 27.778 100 2

215 KF PBS and CVF 22.222 100 1.8

216 CCF PBS and BQF 25 100 1.895

217 KF PBS and BQF 25 100 1.8

218 KF PBS and CCF 33.333 100 1.8

219 SBF CKF and TF 25 100 1.8

220 CCF CKF and TF 25 100 1.895

221 BQF CKF and CVF 36.111 100 2

222 CVF CKF and BQF 36.111 100 2.118

223 CCF TF and HSC 30.556 100 1.895

224 CCF TF and BQF 33.333 100 1.895

225 BQF HSC and CVF 36.111 100 2

226 BQF CVF and SBF 38.889 100 2

227 KF CCF 52.778 94.737 1.705

228 HSS PBS 50 94.444 1.7

229 KF BQF and CCF 44.444 93.75 1.688

230 CCF BQF and KF 44.444 93.75 1.776

231 KF TF and CCF 41.667 93.333 1.68

232 CCF TF and KF 41.667 93.333 1.768

104

233 SBF CVF and BQF 41.667 93.333 1.68

234 CVF BQF and SBF 41.667 93.333 1.976

235 KF SBF and CCF 41.667 93.333 1.68

236 CCF SBF and KF 41.667 93.333 1.768

237 CBQ CKF 38.889 92.857 2.571

238 HSC CKF 38.889 92.857 1.857

239 CVF CKF 38.889 92.857 1.966

240 BQF CKF 38.889 92.857 1.857

241 SBF CKF 38.889 92.857 1.671

242 CCF TF and SBF 38.889 92.857 1.759

243 CVF HSC and BQF 38.889 92.857 1.966

244 KF HSC and CCF 38.889 92.857 1.671

245 CCF HSC and KF 38.889 92.857 1.759

246 BQF CVF and CCF 38.889 92.857 1.857

247 KF CVF and CCF 38.889 92.857 1.671

248 CBQ YF 36.111 92.308 2.556

249 YF CBQ 36.111 92.308 2.556

250 CKF YF 36.111 92.308 2.374

251 CVF YF 36.111 92.308 1.955

252 BQF YF 36.111 92.308 1.846

253 HSC CBQ 36.111 92.308 1.846

254 SBF CBQ 36.111 92.308 1.662

255 YF CBQ and CKF 36.111 92.308 2.556

256 YF CBQ and CVF 36.111 92.308 2.556

257 YF CBQ and BQF 36.111 92.308 2.556

258 YF CKF and CVF 36.111 92.308 2.556

259 YF CKF and BQF 36.111 92.308 2.556

260 CBF PBS and KF 36.111 92.308 1.749

261 HSS PBS and TF 36.111 92.308 1.662

262 HSS PBS and KF 36.111 92.308 1.662

263 HSC CBQ and CKF 36.111 92.308 1.846

264 CBQ CKF and HSC 36.111 92.308 2.556

265 SBF CBQ and CKF 36.111 92.308 1.662

266 CBQ CKF and SBF 36.111 92.308 2.556

267 HSC CBQ and CVF 36.111 92.308 1.846

268 CBQ HSC and CVF 36.111 92.308 2.556

269 HSC CBQ and BQF 36.111 92.308 1.846

270 SBF CBQ and CVF 36.111 92.308 1.662

271 SBF CBQ and BQF 36.111 92.308 1.662

272 KF PBS and TF 36.111 92.308 1.662

273 TF PBS and KF 36.111 92.308 1.846

105

274 CCF PBS and KF 36.111 92.308 1.749

275 CVF CKF and HSC 36.111 92.308 1.955

276 HSC CKF and CVF 36.111 92.308 1.846

277 CKF HSC and CVF 36.111 92.308 2.374

278 BQF CKF and HSC 36.111 92.308 1.846

279 HSC CKF and BQF 36.111 92.308 1.846

280 SBF CKF and HSC 36.111 92.308 1.662

281 HSC CKF and SBF 36.111 92.308 1.846

282 SBF CKF and CVF 36.111 92.308 1.662

283 CVF CKF and SBF 36.111 92.308 1.955

284 SBF CKF and BQF 36.111 92.308 1.662

285 BQF CKF and SBF 36.111 92.308 1.846

286 SBF HSC and CVF 36.111 92.308 1.662

287 PSF YF and CBQ 33.333 91.667 3

288 PSF YF and CKF 33.333 91.667 3

289 PSF YF and CVF 33.333 91.667 3

290 PSF YF and BQF 33.333 91.667 3

291 PSF CBQ and HSC 33.333 91.667 3

292 HSC YF and CBQ 33.333 91.667 1.833

293 YF CBQ and HSC 33.333 91.667 2.538

294 SBF YF and CBQ 33.333 91.667 1.65

295 YF CBQ and SBF 33.333 91.667 2.538

296 HSC YF and CKF 33.333 91.667 1.833

297 SBF YF and CKF 33.333 91.667 1.65

298 HSC YF and CVF 33.333 91.667 1.833

299 HSC YF and BQF 33.333 91.667 1.833

300 SBF YF and CVF 33.333 91.667 1.65

301 SBF YF and BQF 33.333 91.667 1.65

302 HSS CBF and TF 33.333 91.667 1.65

303 CBF HSS and TF 33.333 91.667 1.737

304 PBS CBF and TF 33.333 91.667 1.833

305 CBF PBS and CCF 33.333 91.667 1.737

306 KF CBF and TF 33.333 91.667 1.65

307 TF CBF and KF 33.333 91.667 1.833

308 CCF CBF and KF 33.333 91.667 1.737

309 PBS HSS and CF 33.333 91.667 1.833

310 HSS CF and PBS 33.333 91.667 1.65

311 HSS PBS and CCF 33.333 91.667 1.65

312 KF HSS and TF 33.333 91.667 1.65

313 TF HSS and KF 33.333 91.667 1.833

314 CCF HSS and KF 33.333 91.667 1.737

106

315 TF CF and PBS 33.333 91.667 1.833

316 PBS CF and TF 33.333 91.667 1.833

317 CCF CF and KF 33.333 91.667 1.737

318 CBQ CKF and CCF 33.333 91.667 2.538

319 CBQ CKF and KF 33.333 91.667 2.538

320 SBF CBQ and HSC 33.333 91.667 1.65

321 HSC CBQ and SBF 33.333 91.667 1.833

322 TF PBS and CCF 33.333 91.667 1.833

323 HSC CKF and CCF 33.333 91.667 1.833

324 HSC CKF and KF 33.333 91.667 1.833

325 CVF CKF and CCF 33.333 91.667 1.941

326 CVF CKF and KF 33.333 91.667 1.941

327 BQF CKF and CCF 33.333 91.667 1.833

328 BQF CKF and KF 33.333 91.667 1.833

329 SBF CKF and CCF 33.333 91.667 1.65

330 SBF CKF and KF 33.333 91.667 1.65

331 KF CKF and CCF 33.333 91.667 1.65

332 CCF CKF and KF 33.333 91.667 1.737

333 CCF TF and CVF 33.333 91.667 1.737

334 KF TF and CVF 33.333 91.667 1.65

335 SBF TF and BQF 33.333 91.667 1.65

336 KF TF and BQF 33.333 91.667 1.65

337 SBF PSF 30.556 90.909 1.636

338 SBF PSF and YF 30.556 90.909 1.636

339 PSF YF and SBF 30.556 90.909 2.975

340 SBF PSF and CBQ 30.556 90.909 1.636

341 SBF PSF and CKF 30.556 90.909 1.636

342 SBF PSF and HSC 30.556 90.909 1.636

343 SBF PSF and CVF 30.556 90.909 1.636

344 SBF PSF and BQF 30.556 90.909 1.636

345 YF CBQ and CCF 30.556 90.909 2.517

346 YF CBQ and KF 30.556 90.909 2.517

347 SBF YF and HSC 30.556 90.909 1.636

348 HSC YF and SBF 30.556 90.909 1.818

349 HSS CBF and CF 30.556 90.909 1.636

350 PBS CBF and CF 30.556 90.909 1.818

351 TF CBF and CF 30.556 90.909 1.818

352 TF CBF and CCF 30.556 90.909 1.818

353 TF HSS and CCF 30.556 90.909 1.818

354 HSC CBQ and CCF 30.556 90.909 1.818

355 HSC CBQ and KF 30.556 90.909 1.818

107

356 SBF CBQ and CCF 30.556 90.909 1.636

357 SBF CBQ and KF 30.556 90.909 1.636

358 KF CBQ and CCF 30.556 90.909 1.636

359 CCF CBQ and KF 30.556 90.909 1.722

360 SBF TF and HSC 30.556 90.909 1.636

361 KF TF and HSC 30.556 90.909 1.636

362 CCF KF 55.556 90 1.705

363 PSF YF and CCF 27.778 90 2.945

364 PSF YF and KF 27.778 90 2.945

365 HSC YF and CCF 27.778 90 1.8

366 HSC YF and KF 27.778 90 1.8

367 SBF YF and CCF 27.778 90 1.62

368 SBF YF and KF 27.778 90 1.62

369 KF YF and CCF 27.778 90 1.62

370 CCF YF and KF 27.778 90 1.705

371 HSS CBF and SBF 27.778 90 1.62

372 CBF HSS and SBF 27.778 90 1.705

373 PBS HSS and SBF 27.778 90 1.8

374 HSS PBS and SBF 27.778 90 1.62

375 TF HSS and SBF 27.778 90 1.8

376 PBS CF and SBF 27.778 90 1.8

377 CF PBS and SBF 27.778 90 1.906

378 TF CF and SBF 27.778 90 1.8

379 CCF CF and CVF 27.778 90 1.705

380 CCF CF and SBF 27.778 90 1.705

381 KF CF and SBF 27.778 90 1.62

382 CCF PBS and SBF 27.778 90 1.705

383 KF PBS and SBF 27.778 90 1.62 Table 8 Scoring for All Rule Sets (South China Region)


1.000 = Hot & Spice Sichuan Flavor

ID Probability Rule

11 1 if Pork steak = True then 1.000

11 1 if Pork steak = True and Yolk = True then 1.000

4 1 if Pork steak = True and Crab flavor = True then 1.000

11 1 if Pork steak = True and Cumin BBQ = True then 1.000

11 1 if Pork steak = True and Chicken flavor = True then 1.000

6 1 if Pork steak = True and Tomato flavor = True then 1.000

11 1 if Pork steak = True and Chives flavor = True then 1.000

11 1 if Pork steak = True and BBQ flavor = True then 1.000

108

10 1 if Pork steak = True and Sauced beef flavor = True then 1.000

9 1 if Pork steak = True and Cheese corn flavor = True then 1.000

9 1 if Pork steak = True and Kimchi flavor = True then 1.000

14 0.929 if Chicken flavor = True then 1.000

13 0.923 if Cumin BBQ = True then 1.000

13 0.923 if Cumin BBQ = True and Chicken flavor = True then 1.000

13 0.923 if Cumin BBQ = True and Chives flavor = True then 1.000

13 0.923 if Cumin BBQ = True and BBQ flavor = True then 1.000

13 0.923 if Chicken flavor = True and Chives flavor = True then 1.000

13 0.923 if Chicken flavor = True and BBQ flavor = True then 1.000

13 0.923 if Chicken flavor = True and Sauced beef flavor = True then 1.000

12 0.917 if Yolk = True and Cumin BBQ = True then 1.000

12 0.917 if Yolk = True and Chicken flavor = True then 1.000

12 0.917 if Yolk = True and Chives flavor = True then 1.000

12 0.917 if Yolk = True and BBQ flavor = True then 1.000

12 0.917 if Cumin BBQ = True and Sauced beef flavor = True then 1.000

12 0.917 if Chicken flavor = True and Cheese corn flavor = True then 1.000

12 0.917 if Chicken flavor = True and Kimchi flavor = True then 1.000

11 0.909 if Yolk = True and Sauced beef flavor = True then 1.000

11 0.909 if Cumin BBQ = True and Cheese corn flavor = True then 1.000

11 0.909 if Cumin BBQ = True and Kimchi flavor = True then 1.000

10 0.9 if Yolk = True and Cheese corn flavor = True then 1.000

10 0.9 if Yolk = True and Kimchi flavor = True then 1.000


1.000 = Chives Flavor

ID Probability Rule

11 1 if Pork steak = True then 1.000

13 1 if Cumin BBQ = True then 1.000

11 1 if Pork steak = True and Yolk = True then 1.000

4 1 if Pork steak = True and Crab flavor = True then 1.000

11 1 if Pork steak = True and Cumin BBQ = True then 1.000

11 1 if Pork steak = True and Chicken flavor = True then 1.000

6 1 if Pork steak = True and Tomato flavor = True then 1.000

11 1 if Pork steak = True and Hot sichuan = True then 1.000

11 1 if Pork steak = True and BBQ flavor = True then 1.000

10 1 if Pork steak = True and Sauced beef flavor = True then 1.000

9 1 if Pork steak = True and Cheese corn flavor = True then 1.000

9 1 if Pork steak = True and Kimchi flavor = True then 1.000

5 1 if Yolk = True and Crab flavor = True then 1.000

12 1 if Yolk = True and Cumin BBQ = True then 1.000

12 1 if Yolk = True and Chicken flavor = True then 1.000

109

7 1 if Yolk = True and Tomato flavor = True then 1.000

11 1 if Yolk = True and Hot sichuan = True then 1.000

12 1 if Yolk = True and BBQ flavor = True then 1.000

11 1 if Yolk = True and Sauced beef flavor = True then 1.000

10 1 if Yolk = True and Cheese corn flavor = True then 1.000

10 1 if Yolk = True and Kimchi flavor = True then 1.000

6 1 if Crab flavor = True and Cumin BBQ = True then 1.000

4 1 if Cumin BBQ = True and Pepper beef steak = True then 1.000

13 1 if Cumin BBQ = True and Chicken flavor = True then 1.000

8 1 if Cumin BBQ = True and Tomato flavor = True then 1.000

12 1 if Cumin BBQ = True and Hot sichuan = True then 1.000

13 1 if Cumin BBQ = True and BBQ flavor = True then 1.000

12 1 if Cumin BBQ = True and Sauced beef flavor = True then 1.000

11 1 if Cumin BBQ = True and Cheese corn flavor = True then 1.000

11 1 if Cumin BBQ = True and Kimchi flavor = True then 1.000

13 1 if Chicken flavor = True and BBQ flavor = True then 1.000

15 0.933 if BBQ flavor = True and Sauced beef flavor = True then 1.000

14 0.929 if Chicken flavor = True then 1.000

14 0.929 if Hot sichuan = True and BBQ flavor = True then 1.000

13 0.923 if Yolk = True then 1.000

13 0.923 if Chicken flavor = True and Hot sichuan = True then 1.000

13 0.923 if Chicken flavor = True and Sauced beef flavor = True then 1.000

12 0.917 if Chicken flavor = True and Cheese corn flavor = True then 1.000

12 0.917 if Chicken flavor = True and Kimchi flavor = True then 1.000


1.000 = Kimchi Flavor

ID Probability Rule

10 1 if Yolk = 1 and Cheese corn flavor = 1 then 1.000

10 1 if Crab flavor = 1 and Sauced beef flavor = 1 then 1.000

10 1 if Pepper beef steak = 1 and Sauced beef flavor = 1 then 1.000

11 1 if Cumin BBQ = 1 and Cheese corn flavor = 1 then 1.000

11 1 if Tomato flavor = 1 and Hot sichuan = 1 then 1.000

12 1 if Curry beef = 1 and Tomato flavor = 1 then 1.000

12 1 if spices salt = 1 and Tomato flavor = 1 then 1.000

12 1 if Chicken flavor = 1 and Cheese corn flavor = 1 then 1.000

12 1 if Tomato flavor = 1 and Chives flavor = 1 then 1.000

12 1 if Tomato flavor = 1 and BBQ flavor = 1 then 1.000

13 1 if Pepper beef steak = 1 and Tomato flavor = 1 then 1.000

14 1 if Hot sichuan = 1 and Cheese corn flavor = 1 then 1.000

14 1 if Chives flavor = 1 and Cheese corn flavor = 1 then 1.000

15 1 if Tomato flavor = 1 and Cheese corn flavor = 1 then 1.000

110

15 1 if Sauced beef flavor = 1 and Cheese corn flavor = 1 then 1.000

16 1 if BBQ flavor = 1 and Cheese corn flavor = 1 then 1.000

19 1 if Cheese corn flavor = 1 then 1.000

4 1 if Pork steak = 1 and Crab flavor = 1 then 1.000

5 1 if Yolk = 1 and Crab flavor = 1 then 1.000

4 1 if Curry beef = 1 and Chicken flavor = 1 then 1.000

7 1 if Curry beef = 1 and Chives flavor = 1 then 1.000

8 0.947 if Curry beef = 1 and BBQ flavor = 1 then 1.000

11 0.938 if Curry beef = 1 and Cheese corn flavor = 1 then 1.000

4 0.933 if spices salt = 1 and Chicken flavor = 1 then 1.000

7 0.933 if spices salt = 1 and Chives flavor = 1 then 1.000

8 0.929 if spices salt = 1 and BBQ flavor = 1 then 1.000

11 0.929 if spices salt = 1 and Cheese corn flavor = 1 then 1.000

6 0.923 if Crab flavor = 1 and Cumin BBQ = 1 then 1.000

7 0.917 if Crab flavor = 1 and Chicken flavor = 1 then 1.000

7 0.917 if Crab flavor = 1 and Hot sichuan = 1 then 1.000

10 0.917 if Crab flavor = 1 and Chives flavor = 1 then 1.000

9 0.917 if Crab flavor = 1 and BBQ flavor = 1 then 1.000

11 0.917 if Crab flavor = 1 and Cheese corn flavor = 1 then 1.000

4 0.909 if Cumin BBQ = 1 and Pepper beef steak = 1 then 1.000

5 0.909 if Pepper beef steak = 1 and Chicken flavor = 1 then 1.000

8 0.9 if Pepper beef steak = 1 and Chives flavor = 1 then 1.000

9 0.9 if Pepper beef steak = 1 and BBQ flavor = 1 then 1.000

12 0.9 if Pepper beef steak = 1 and Cheese corn flavor = 1 then 1.000


1.000 = Yolk Flavor

ID Probability Rule

11 1 if Pork steak = 1 then 1.000


11 1 if Pork steak = 1 and Cumin BBQ = 1 then 1.000

11 1 if Pork steak = 1 and Chicken flavor = 1 then 1.000

6 1 if Pork steak = 1 and Tomato flavor = 1 then 1.000

11 1 if Pork steak = 1 and Hot sichuan = 1 then 1.000

11 1 if Pork steak = 1 and Chives flavor = 1 then 1.000

11 1 if Pork steak = 1 and BBQ flavor = 1 then 1.000

10 1 if Pork steak = 1 and Sauced beef flavor = 1 then 1.000

9 1 if Pork steak = 1 and Cheese corn flavor = 1 then 1.000

9 1 if Pork steak = 1 and Kimchi flavor = 1 then 1.000

13 0.923 if Cumin BBQ = 1 then 1.000

13 0.923 if Cumin BBQ = 1 and Chicken flavor = 1 then 1.000

13 0.923 if Cumin BBQ = 1 and Chives flavor = 1 then 1.000

111

13 0.923 if Cumin BBQ = 1 and BBQ flavor = 1 then 1.000

13 0.923 if Chicken flavor = 1 and Chives flavor = 1 then 1.000

13 0.923 if Chicken flavor = 1 and BBQ flavor = 1 then 1.000

12 0.917 if Cumin BBQ = 1 and Hot sichuan = 1 then 1.000

12 0.917 if Cumin BBQ = 1 and Sauced beef flavor = 1 then 1.000

11 0.909 if Cumin BBQ = 1 and Cheese corn flavor = 1 then 1.000

11 0.909 if Cumin BBQ = 1 and Kimchi flavor = 1 then 1.000


1.000 = Pork Steak Flavor

ID Probability Rule

11 1 if Yolk = 1 and Hot sichuan = 1 then 1.000

12 0.917 if Yolk = 1 and Cumin BBQ = 1 then 1.000

12 0.917 if Yolk = 1 and Chicken flavor = 1 then 1.000

12 0.917 if Yolk = 1 and Chives flavor = 1 then 1.000

12 0.917 if Yolk = 1 and BBQ flavor = 1 then 1.000


11 0.909 if Yolk = 1 and Sauced beef flavor = 1 then 1.000

10 0.9 if Yolk = 1 and Cheese corn flavor = 1 then 1.000

10 0.9 if Yolk = 1 and Kimchi flavor = 1 then 1.000


1.000 = Hot & Spice Salt Flavor

ID Probability Rule

15 1 if Curry beef = 1 and Pepper beef steak = 1 then 1.000


8 1 if Curry beef = 1 and Hot sichuan = 1 then 1.000


8 1 if Curry beef = 1 and BBQ flavor = 1 then 1.000

11 1 if Curry beef = 1 and Cheese corn flavor = 1 then 1.000

12 1 if Curry beef = 1 and Kimchi flavor = 1 then 1.000

18 0.944 if Pepper beef steak = 1 then 1.000

13 0.923 if Pepper beef steak = 1 and Tomato flavor = 1 then 1.000

13 0.923 if Pepper beef steak = 1 and Kimchi flavor = 1 then 1.000

12 0.917 if Curry beef = 1 and Tomato flavor = 1 then 1.000

12 0.917 if Crab flavor = 1 and Pepper beef steak = 1 then 1.000


11 0.909 if Curry beef = 1 and Crab flavor = 1 then 1.000

10 0.9 if Curry beef = 1 and Sauced beef flavor = 1 then 1.000

10 0.9 if Pepper beef steak = 1 and Sauced beef flavor = 1 then 1.000


1.000 = Cumin BBQ Flavor

ID Probability Rule

112


11 1 if Pork steak = 1 and Yolk = 1 then 1.000











12 1 if Yolk = 1 and Chicken flavor = 1 then 1.000

7 1 if Yolk = 1 and Tomato flavor = 1 then 1.000


12 1 if Yolk = 1 and Chives flavor = 1 then 1.000

12 1 if Yolk = 1 and BBQ flavor = 1 then 1.000

11 1 if Yolk = 1 and Sauced beef flavor = 1 then 1.000


10 1 if Yolk = 1 and Kimchi flavor = 1 then 1.000

13 1 if Chicken flavor = 1 and Chives flavor = 1 then 1.000

13 1 if Chicken flavor = 1 and BBQ flavor = 1 then 1.000

14 0.929 if Chicken flavor = 1 then 1.000

13 0.923 if Yolk = 1 then 1.000

13 0.923 if Chicken flavor = 1 and Hot sichuan = 1 then 1.000

13 0.923 if Chicken flavor = 1 and Sauced beef flavor = 1 then 1.000

13 0.923 if Hot sichuan = 1 and Chives flavor = 1 then 1.000

12 0.917 if Chicken flavor = 1 and Cheese corn flavor = 1 then 1.000

12 0.917 if Chicken flavor = 1 and Kimchi flavor = 1 then 1.000


1.000 = Curry Beef Flavor

ID Probability Rule

4 1 if spices salt = 1 and Chicken flavor = 1 then 1.000

8 1 if spices salt = 1 and Hot sichuan = 1 then 1.000

7 1 if spices salt = 1 and Chives flavor = 1 then 1.000

8 1 if spices salt = 1 and BBQ flavor = 1 then 1.000

11 1 if spices salt = 1 and Cheese corn flavor = 1 then 1.000

12 1 if spices salt = 1 and Kimchi flavor = 1 then 1.000


12 0.917 if spices salt = 1 and Tomato flavor = 1 then 1.000


113

10 0.9 if spices salt = 1 and Sauced beef flavor = 1 then 1.000


1.000 = Chicken Flavor

ID Probability Rule


13 1 if Cumin BBQ = 1 then 1.000












12 1 if Yolk = 1 and Cumin BBQ = 1 then 1.000








6 1 if Crab flavor = 1 and Cumin BBQ = 1 then 1.000

4 1 if Cumin BBQ = 1 and Pepper beef steak = 1 then 1.000

8 1 if Cumin BBQ = 1 and Tomato flavor = 1 then 1.000

12 1 if Cumin BBQ = 1 and Hot sichuan = 1 then 1.000

13 1 if Cumin BBQ = 1 and Chives flavor = 1 then 1.000

13 1 if Cumin BBQ = 1 and BBQ flavor = 1 then 1.000

12 1 if Cumin BBQ = 1 and Sauced beef flavor = 1 then 1.000


11 1 if Cumin BBQ = 1 and Kimchi flavor = 1 then 1.000

13 0.923 if Yolk = 1 then 1.000



1.000 = Pepper Beef Steak Flavor

ID Probability Rule



114









12 0.917 if spices salt = 1 and Crab flavor = 1 then 1.000

12 0.917 if Crab flavor = 1 and Tomato flavor = 1 then 1.000



10 0.9 if Crab flavor = 1 and Sauced beef flavor = 1 then 1.000

BBQ Flavor Rule Set

1.000 = BBQ Flavor

ID Probability Rule


12 1 if Chicken flavor = 1 and Kimchi flavor = 1 then 1.000

13 1 if Yolk = 1 then 1.000

13 1 if Chicken flavor = 1 and Hot sichuan = 1 then 1.000

13 1 if Chicken flavor = 1 and Sauced beef flavor = 1 then 1.000

14 1 if Chicken flavor = 1 then 1.000




















115






13 1 if Cumin BBQ = 1 and Chicken flavor = 1 then 1.000









14 0.917 if Chives flavor = 1 and Sauced beef flavor = 1 then 1.000


1.000 = Cheese Corn Flavor

ID Probability Rule

20 1 if Kimchi flavor = 1 then 1.000


10 1 if Crab flavor = 1 and Chives flavor = 1 then 1.000






12 1 if Crab flavor = 1 and Kimchi flavor = 1 then 1.000


12 1 if Tomato flavor = 1 and Chives flavor = 1 then 1.000

13 1 if Pepper beef steak = 1 and Kimchi flavor = 1 then 1.000

14 1 if Tomato flavor = 1 and Sauced beef flavor = 1 then 1.000

14 1 if Hot sichuan = 1 and Kimchi flavor = 1 then 1.000

15 1 if Tomato flavor = 1 and Kimchi flavor = 1 then 1.000

15 1 if Sauced beef flavor = 1 and Kimchi flavor = 1 then 1.000

16 1 if BBQ flavor = 1 and Kimchi flavor = 1 then 1.000



5 0.938 if Yolk = 1 and Crab flavor = 1 then 1.000

7 0.933 if Yolk = 1 and Tomato flavor = 1 then 1.000

4 0.933 if Curry beef = 1 and Chicken flavor = 1 then 1.000


116

4 0.929 if spices salt = 1 and Chicken flavor = 1 then 1.000

8 0.923 if spices salt = 1 and BBQ flavor = 1 then 1.000





4 0.917 if Cumin BBQ = 1 and Pepper beef steak = 1 then 1.000

8 0.909 if Cumin BBQ = 1 and Tomato flavor = 1 then 1.000

5 0.9 if Pepper beef steak = 1 and Chicken flavor = 1 then 1.000

9 0.9 if Pepper beef steak = 1 and BBQ flavor = 1 then 1.000

9 0.9 if Chicken flavor = 1 and Tomato flavor = 1 then 1.000

11 0.9 if Tomato flavor = 1 and Hot sichuan = 1 then 1.000

12 0.9 if Tomato flavor = 1 and BBQ flavor = 1 then 1.000


1.000 = Sauced Beef Flavor

ID Probability Rule







5 1 if Pepper beef steak = 1 and Chicken flavor = 1 then 1.000

9 1 if Chicken flavor = 1 and Tomato flavor = 1 then 1.000


13 0.923 if Cumin BBQ = 1 then 1.000








15 0.923 if Chives flavor = 1 and BBQ flavor = 1 then 1.000








117


11 0.909 if Pork steak = 1 then 1.000

11 0.909 if Pork steak = 1 and Yolk = 1 then 1.000

11 0.909 if Pork steak = 1 and Cumin BBQ = 1 then 1.000

11 0.909 if Pork steak = 1 and Chicken flavor = 1 then 1.000

11 0.909 if Pork steak = 1 and Hot sichuan = 1 then 1.000

11 0.909 if Pork steak = 1 and Chives flavor = 1 then 1.000

11 0.909 if Pork steak = 1 and BBQ flavor = 1 then 1.000

11 0.909 if Yolk = 1 and Hot sichuan = 1 then 1.000







1.000 = Crab Flavor

ID Probability Rule







8 1 if Pepper beef steak = 1 and Chives flavor = 1 then 1.000



1.000 = Tomato Flavor

ID Probability Rule










12 0.917 if Curry beef = 1 and Kimchi flavor = 1 then 1.000

12 0.917 if spices salt = 1 and Kimchi flavor = 1 then 1.000



118





10 0.9 if Crab flavor = 1 and Sauced beef flavor = 1 then 1.000 Table 9 Scoring information for Top 10 Rules in South China Region Month Jan. Feb. Mar. Apr. May. Jun. Jul. Aug. Sep. Oct.

TF 1 0 0 1 0 0 1 1 1 0

CF 1 0 1 1 1 1 1 1 1 0

SBF 1 0 0 0 0 0 1 1 1 1

CCF 0 0 0 0 0 0 1 1 1 0

BQF 0 0 0 0 0 0 1 1 1 0

PBS 1 0 1 0 0 0 1 1 1 0

CKF 0 0 0 0 0 0 0 0 1 0

CBF 0 0 1 1 0 0 1 1 1 1

CBQ 0 0 0 0 0 0 0 0 1 0

HSS 1 0 1 0 0 1 1 1 1 0

PSF 0 0 0 0 0 0 0 0 0 0

YF 0 0 0 0 0 0 0 0 1 0

Kimchi flavor

0 0 0 0 0 0 1 1 1 0

Chives flavor 0 0 0 0 0 0 0 1 1 0

Hot sichuan 0 0 0 0 0 0 0 0 0 0

Scoring Information

Rule ID 1 CC TF PBS PBS CVF HSC HSS

Prediction 1 0.93 0.92 0.92 0.92 0.93 0.93 0.9

Confidence 1

367 249 250 213 377 20 182

Rule ID 2 KF HSS PSF

Prediction 2 0.92 0.92 0.92

Confidence 2

325 174 29

Rule ID 3 CBF KF

Prediction 3 0.92 0.92

Confidence 3

175 206

Table 10 Results for the Mid China


1 CVF PSF 33.333 100 3

2 PSF CVF 33.333 100 3

3 Yolk PSF 33.333 100 2.769

119

4 BQF PSF 33.333 100 2.4 5 Yolk CVF 33.333 100 2.769

6 BQF CVF 33.333 100 2.4 7 HSS PBS 47.222 100 1.895

8 BQF Yolk 36.111 100 2.4

9 CKF CBQ 36.111 100 2.571 10 BQF CBQ 36.111 100 2.4

11 BQF CKF 38.889 100 2.4 12 HSS CBF and PBS 44.444 100 1.895

13 PBS CBF and CF 19.444 100 2.118

14 PBS CBF and CKF 11.111 100 2.118 15 PBS CBF and TF 22.222 100 2.118

16 PBS CBF and BQF 11.111 100 2.118 17 PBS CBF and HSC 25 100 2.118

18 PBS CBF and CCF 30.556 100 2.118 19 PBS CBF and KF 27.778 100 2.118

20 HSS CBF and CF 19.444 100 1.895

21 HSS CBF and CKF 11.111 100 1.895 22 HSS CBF and TF 22.222 100 1.895

23 HSS CBF and BQF 11.111 100 1.895 24 HSS CBF and HSC 25 100 1.895

25 HSS CBF and CCF 30.556 100 1.895

26 HSS CBF and KF 27.778 100 1.895 27 KF CBF and CF 19.444 100 1.895

28 TF CBF and CKF 11.111 100 2.25 29 BQF CBF and CKF 11.111 100 2.4

30 CKF CBF and BQF 11.111 100 2.571 31 CCF CBF and CKF 11.111 100 1.8

32 SBF CBF and CKF 11.111 100 1.8

33 TF CBF and BQF 11.111 100 2.25 34 CCF CBF and BQF 11.111 100 1.8

35 SBF CBF and BQF 11.111 100 1.8 36 CVF PSF and CF 19.444 100 3

37 PSF CVF and CF 19.444 100 3

38 Yolk PSF and CVF 33.333 100 2.769 39 CVF PSF and Yolk 33.333 100 3

40 PSF CVF and Yolk 33.333 100 3 41 CVF PSF and CBQ 30.556 100 3

42 PSF CVF and CBQ 30.556 100 3 43 CVF PSF and CKF 30.556 100 3

44 PSF CVF and CKF 30.556 100 3

120

45 CVF PSF and TF 22.222 100 3 46 PSF CVF and TF 22.222 100 3

47 BQF PSF and CVF 33.333 100 2.4 48 CVF PSF and BQF 33.333 100 3

49 PSF CVF and BQF 33.333 100 3

50 CVF PSF and HSC 30.556 100 3 51 PSF CVF and HSC 30.556 100 3

52 CVF PSF and CCF 25 100 3 53 PSF CVF and CCF 25 100 3

54 CVF PSF and SBF 25 100 3

55 PSF CVF and SBF 25 100 3 56 CVF PSF and KF 27.778 100 3

57 PSF CVF and KF 27.778 100 3 58 Yolk PSF and CF 19.444 100 2.769

59 PSF CF and Yolk 19.444 100 3 60 CBQ PSF and CF 19.444 100 2.769

61 CKF PSF and CF 19.444 100 2.571

62 BQF PSF and CF 19.444 100 2.4 63 HSC PSF and CF 19.444 100 1.895

64 SBF PSF and CF 19.444 100 1.8 65 KF PSF and CF 19.444 100 1.895

66 Yolk PSF and CBQ 30.556 100 2.769

67 Yolk PSF and CKF 30.556 100 2.769 68 Yolk PSF and TF 22.222 100 2.769

69 BQF PSF and Yolk 33.333 100 2.4 70 Yolk PSF and BQF 33.333 100 2.769

71 Yolk PSF and HSC 30.556 100 2.769 72 PSF Yolk and HSC 30.556 100 3

73 Yolk PSF and CCF 25 100 2.769

74 Yolk PSF and SBF 25 100 2.769 75 Yolk PSF and KF 27.778 100 2.769

76 CKF PSF and CBQ 30.556 100 2.571 77 CBQ PSF and CKF 30.556 100 2.769

78 CBQ PSF and TF 22.222 100 2.769

79 BQF PSF and CBQ 30.556 100 2.4 80 HSC PSF and CBQ 30.556 100 1.895

81 CBQ PSF and HSC 30.556 100 2.769 82 CBQ PSF and CCF 25 100 2.769

83 CBQ PSF and SBF 25 100 2.769 84 CBQ PSF and KF 27.778 100 2.769

85 CKF PSF and TF 22.222 100 2.571

121

86 BQF PSF and CKF 30.556 100 2.4 87 HSC PSF and CKF 30.556 100 1.895

88 CKF PSF and HSC 30.556 100 2.571 89 CKF PSF and CCF 25 100 2.571

90 CKF PSF and SBF 25 100 2.571

91 CKF PSF and KF 27.778 100 2.571 92 BQF PSF and TF 22.222 100 2.4

93 HSC PSF and TF 22.222 100 1.895 94 BQF PSF and HSC 30.556 100 2.4

95 BQF PSF and CCF 25 100 2.4

96 BQF PSF and SBF 25 100 2.4 97 BQF PSF and KF 27.778 100 2.4

98 HSC PSF and CCF 25 100 1.895 99 HSC PSF and SBF 25 100 1.895

100 HSC PSF and KF 27.778 100 1.895 101 KF PSF and CCF 25 100 1.895

102 KF PSF and SBF 25 100 1.895

103 Yolk CVF and CF 19.444 100 2.769 104 CVF CF and Yolk 19.444 100 3

105 CBQ CVF and CF 19.444 100 2.769 106 CKF CVF and CF 19.444 100 2.571

107 BQF CVF and CF 19.444 100 2.4

108 HSC CVF and CF 19.444 100 1.895 109 SBF CVF and CF 19.444 100 1.8

110 KF CVF and CF 19.444 100 1.895 111 Yolk CVF and CBQ 30.556 100 2.769

112 Yolk CVF and CKF 30.556 100 2.769 113 Yolk CVF and TF 22.222 100 2.769

114 BQF CVF and Yolk 33.333 100 2.4

115 Yolk CVF and BQF 33.333 100 2.769 116 Yolk CVF and HSC 30.556 100 2.769

117 CVF Yolk and HSC 30.556 100 3 118 Yolk CVF and CCF 25 100 2.769

119 Yolk CVF and SBF 25 100 2.769

120 Yolk CVF and KF 27.778 100 2.769 121 CKF CVF and CBQ 30.556 100 2.571

122 CBQ CVF and CKF 30.556 100 2.769 123 CBQ CVF and TF 22.222 100 2.769

124 BQF CVF and CBQ 30.556 100 2.4 125 HSC CVF and CBQ 30.556 100 1.895

126 CBQ CVF and HSC 30.556 100 2.769

122

127 CBQ CVF and CCF 25 100 2.769 128 CBQ CVF and SBF 25 100 2.769

129 CBQ CVF and KF 27.778 100 2.769 130 CKF CVF and TF 22.222 100 2.571

131 BQF CVF and CKF 30.556 100 2.4

132 HSC CVF and CKF 30.556 100 1.895 133 CKF CVF and HSC 30.556 100 2.571

134 CKF CVF and CCF 25 100 2.571 135 CKF CVF and SBF 25 100 2.571

136 CKF CVF and KF 27.778 100 2.571

137 BQF CVF and TF 22.222 100 2.4 138 HSC CVF and TF 22.222 100 1.895

139 BQF CVF and HSC 30.556 100 2.4 140 BQF CVF and CCF 25 100 2.4

141 BQF CVF and SBF 25 100 2.4 142 BQF CVF and KF 27.778 100 2.4

143 HSC CVF and CCF 25 100 1.895

144 HSC CVF and SBF 25 100 1.895 145 HSC CVF and KF 27.778 100 1.895

146 KF CVF and CCF 25 100 1.895 147 KF CVF and SBF 25 100 1.895

148 HSS PBS and CF 22.222 100 1.895

149 PBS HSS and CF 22.222 100 2.118 150 HSS PBS and CBQ 11.111 100 1.895

151 PBS HSS and CBQ 11.111 100 2.118 152 HSS PBS and CKF 13.889 100 1.895

153 PBS HSS and CKF 13.889 100 2.118 154 HSS PBS and TF 25 100 1.895

155 PBS HSS and TF 25 100 2.118

156 HSS PBS and BQF 13.889 100 1.895 157 PBS HSS and BQF 13.889 100 2.118

158 HSS PBS and HSC 27.778 100 1.895 159 PBS HSS and HSC 27.778 100 2.118

160 HSS PBS and CCF 33.333 100 1.895

161 PBS HSS and CCF 33.333 100 2.118 162 HSS PBS and SBF 30.556 100 1.895

163 HSS PBS and KF 30.556 100 1.895 164 PBS HSS and KF 30.556 100 2.118

165 KF PBS and CF 22.222 100 1.895 166 CKF PBS and CBQ 11.111 100 2.571

167 TF PBS and CBQ 11.111 100 2.25

123

168 BQF PBS and CBQ 11.111 100 2.4 169 CCF PBS and CBQ 11.111 100 1.8

170 SBF PBS and CBQ 11.111 100 1.8 171 KF PBS and CBQ 11.111 100 1.895

172 TF PBS and CKF 13.889 100 2.25

173 BQF PBS and CKF 13.889 100 2.4 174 CKF PBS and BQF 13.889 100 2.571

175 CCF PBS and CKF 13.889 100 1.8 176 SBF PBS and CKF 13.889 100 1.8

177 TF PBS and BQF 13.889 100 2.25

178 CCF PBS and BQF 13.889 100 1.8 179 SBF PBS and BQF 13.889 100 1.8

180 KF HSS and CF 22.222 100 1.895 181 CKF HSS and CBQ 11.111 100 2.571

182 TF HSS and CBQ 11.111 100 2.25 183 BQF HSS and CBQ 11.111 100 2.4

184 CCF HSS and CBQ 11.111 100 1.8

185 SBF HSS and CBQ 11.111 100 1.8 186 KF HSS and CBQ 11.111 100 1.895

187 TF HSS and CKF 13.889 100 2.25 188 BQF HSS and CKF 13.889 100 2.4

189 CKF HSS and BQF 13.889 100 2.571

190 CCF HSS and CKF 13.889 100 1.8 191 SBF HSS and CKF 13.889 100 1.8

192 TF HSS and BQF 13.889 100 2.25 193 CCF HSS and BQF 13.889 100 1.8

194 SBF HSS and BQF 13.889 100 1.8 195 CBQ CF and Yolk 19.444 100 2.769

196 CKF CF and Yolk 19.444 100 2.571

197 BQF CF and Yolk 19.444 100 2.4 198 HSC CF and Yolk 19.444 100 1.895

199 SBF CF and Yolk 19.444 100 1.8 200 KF CF and Yolk 19.444 100 1.895

201 CKF CF and CBQ 22.222 100 2.571

202 CBQ CF and CKF 22.222 100 2.769 203 BQF CF and CBQ 22.222 100 2.4

204 CBQ CF and BQF 22.222 100 2.769 205 HSC CF and CBQ 22.222 100 1.895

206 SBF CF and CBQ 22.222 100 1.8 207 KF CF and CBQ 22.222 100 1.895

208 BQF CF and CKF 22.222 100 2.4

124

209 CKF CF and BQF 22.222 100 2.571 210 HSC CF and CKF 22.222 100 1.895

211 SBF CF and CKF 22.222 100 1.8 212 KF CF and CKF 22.222 100 1.895

213 HSC CF and TF 25 100 1.895

214 SBF CF and TF 25 100 1.8 215 KF CF and TF 25 100 1.895

216 HSC CF and BQF 22.222 100 1.895 217 SBF CF and BQF 22.222 100 1.8

218 KF CF and BQF 22.222 100 1.895

219 KF CF and HSC 30.556 100 1.895 220 KF CF and CCF 27.778 100 1.895

221 KF CF and SBF 33.333 100 1.895 222 CKF Yolk and CBQ 33.333 100 2.571

223 CBQ Yolk and CKF 33.333 100 2.769 224 CBQ Yolk and TF 25 100 2.769

225 BQF Yolk and CBQ 33.333 100 2.4

226 CBQ Yolk and HSC 30.556 100 2.769 227 CBQ Yolk and CCF 27.778 100 2.769

228 CBQ Yolk and SBF 27.778 100 2.769 229 CBQ Yolk and KF 30.556 100 2.769

230 CKF Yolk and TF 25 100 2.571

231 BQF Yolk and CKF 33.333 100 2.4 232 CKF Yolk and HSC 30.556 100 2.571

233 CKF Yolk and CCF 27.778 100 2.571 234 CKF Yolk and SBF 27.778 100 2.571

235 CKF Yolk and KF 30.556 100 2.571 236 BQF Yolk and TF 25 100 2.4

237 BQF Yolk and HSC 30.556 100 2.4

238 BQF Yolk and CCF 27.778 100 2.4 239 BQF Yolk and SBF 27.778 100 2.4

240 BQF Yolk and KF 30.556 100 2.4 241 KF Yolk and CCF 27.778 100 1.895

242 KF Yolk and SBF 27.778 100 1.895

243 CKF CBQ and TF 27.778 100 2.571 244 BQF CBQ and CKF 36.111 100 2.4

245 CKF CBQ and BQF 36.111 100 2.571 246 CKF CBQ and HSC 33.333 100 2.571

247 CBQ CKF and HSC 33.333 100 2.769 248 CKF CBQ and CCF 30.556 100 2.571

249 CKF CBQ and SBF 30.556 100 2.571

125

250 CKF CBQ and KF 33.333 100 2.571 251 CBQ CKF and KF 33.333 100 2.769

252 BQF CBQ and TF 27.778 100 2.4 253 BQF CBQ and HSC 33.333 100 2.4

254 CBQ BQF and HSC 33.333 100 2.769

255 BQF CBQ and CCF 30.556 100 2.4 256 BQF CBQ and SBF 30.556 100 2.4

257 BQF CBQ and KF 33.333 100 2.4 258 CBQ BQF and KF 33.333 100 2.769

259 KF CBQ and CCF 30.556 100 1.895

260 KF CBQ and SBF 30.556 100 1.895 261 BQF CKF and TF 30.556 100 2.4

262 CKF TF and BQF 30.556 100 2.571 263 BQF CKF and HSC 33.333 100 2.4

264 CKF BQF and HSC 33.333 100 2.571 265 BQF CKF and CCF 33.333 100 2.4

266 CKF BQF and CCF 33.333 100 2.571

267 BQF CKF and SBF 33.333 100 2.4 268 CKF BQF and SBF 33.333 100 2.571

269 BQF CKF and KF 33.333 100 2.4 270 CKF BQF and KF 33.333 100 2.571

271 SBF TF and KF 33.333 100 1.8

272 KF HSC and SBF 38.889 100 1.895 273 HSS CBF 50 94.444 1.789

274 CBF PBS 47.222 94.118 1.882 275 PBS CBF and HSS 47.222 94.118 1.993

276 CBF PBS and HSS 47.222 94.118 1.882 277 CKF BQF 41.667 93.333 2.4

278 KF HSC and CCF 41.667 93.333 1.768

279 KF CCF and SBF 41.667 93.333 1.768 280 KF CF 38.889 92.857 1.759

281 CBQ CKF 38.889 92.857 2.571 282 CBQ CKF and BQF 38.889 92.857 2.571

283 PSF Yolk 36.111 92.308 2.769

284 CVF Yolk 36.111 92.308 2.769 285 CBQ Yolk 36.111 92.308 2.556

286 Yolk CBQ 36.111 92.308 2.556 287 CKF Yolk 36.111 92.308 2.374

288 HSC CBQ 36.111 92.308 1.749 289 KF CBQ 36.111 92.308 1.749

290 PSF Yolk and BQF 36.111 92.308 2.769

126

291 CVF Yolk and BQF 36.111 92.308 2.769 292 SBF CF and KF 36.111 92.308 1.662

293 Yolk CBQ and CKF 36.111 92.308 2.556 294 CBQ Yolk and BQF 36.111 92.308 2.556

295 Yolk CBQ and BQF 36.111 92.308 2.556

296 CKF Yolk and BQF 36.111 92.308 2.374 297 HSC CBQ and CKF 36.111 92.308 1.749

298 KF CBQ and CKF 36.111 92.308 1.749 299 HSC CBQ and BQF 36.111 92.308 1.749

300 KF CBQ and BQF 36.111 92.308 1.749

301 KF TF and SBF 36.111 92.308 1.749 302 CBQ PSF 33.333 91.667 2.538

303 CKF PSF 33.333 91.667 2.357 304 HSC PSF 33.333 91.667 1.737

305 CBQ CVF 33.333 91.667 2.538 306 CKF CVF 33.333 91.667 2.357

307 HSC CVF 33.333 91.667 1.737

308 CBF PBS and CCF 33.333 91.667 1.833 309 CBF HSS and CCF 33.333 91.667 1.833

310 HSS CBF and SBF 33.333 91.667 1.737 311 CBF HSS and SBF 33.333 91.667 1.833

312 CBQ PSF and CVF 33.333 91.667 2.538

313 CKF PSF and CVF 33.333 91.667 2.357 314 HSC PSF and CVF 33.333 91.667 1.737

315 CBQ PSF and Yolk 33.333 91.667 2.538 316 PSF Yolk and CBQ 33.333 91.667 2.75

317 CKF PSF and Yolk 33.333 91.667 2.357 318 PSF Yolk and CKF 33.333 91.667 2.75

319 HSC PSF and Yolk 33.333 91.667 1.737

320 CBQ PSF and BQF 33.333 91.667 2.538 321 PSF CBQ and HSC 33.333 91.667 2.75

322 CKF PSF and BQF 33.333 91.667 2.357 323 PSF CKF and HSC 33.333 91.667 2.75

324 HSC PSF and BQF 33.333 91.667 1.737

325 PSF BQF and HSC 33.333 91.667 2.75 326 CBQ CVF and Yolk 33.333 91.667 2.538

327 CVF Yolk and CBQ 33.333 91.667 2.75 328 CKF CVF and Yolk 33.333 91.667 2.357

329 CVF Yolk and CKF 33.333 91.667 2.75 330 HSC CVF and Yolk 33.333 91.667 1.737

331 CBQ CVF and BQF 33.333 91.667 2.538

127

332 CVF CBQ and HSC 33.333 91.667 2.75 333 CKF CVF and BQF 33.333 91.667 2.357

334 CVF CKF and HSC 33.333 91.667 2.75 335 HSC CVF and BQF 33.333 91.667 1.737

336 CVF BQF and HSC 33.333 91.667 2.75

337 PBS HSS and SBF 33.333 91.667 1.941 338 HSC Yolk and CBQ 33.333 91.667 1.737

339 Yolk CBQ and HSC 33.333 91.667 2.538 340 KF Yolk and CBQ 33.333 91.667 1.737

341 Yolk CBQ and KF 33.333 91.667 2.538

342 HSC Yolk and CKF 33.333 91.667 1.737 343 Yolk CKF and HSC 33.333 91.667 2.538

344 KF Yolk and CKF 33.333 91.667 1.737 345 Yolk CKF and KF 33.333 91.667 2.538

346 Yolk BQF and HSC 33.333 91.667 2.538 347 Yolk BQF and KF 33.333 91.667 2.538

348 CBQ CKF and CCF 33.333 91.667 2.538

349 CBQ CKF and SBF 33.333 91.667 2.538 350 CBQ BQF and CCF 33.333 91.667 2.538

351 CBQ BQF and SBF 33.333 91.667 2.538 352 KF CBQ and HSC 33.333 91.667 1.737

353 HSC CBQ and KF 33.333 91.667 1.737

354 CCF CBQ and KF 33.333 91.667 1.65 355 SBF CBQ and KF 33.333 91.667 1.65

356 KF CKF and HSC 33.333 91.667 1.737 357 HSC CKF and KF 33.333 91.667 1.737

358 SBF CKF and CCF 33.333 91.667 1.65 359 CCF CKF and SBF 33.333 91.667 1.65

360 KF CKF and CCF 33.333 91.667 1.737

361 CCF CKF and KF 33.333 91.667 1.65 362 KF CKF and SBF 33.333 91.667 1.737

363 SBF CKF and KF 33.333 91.667 1.65 364 SBF TF and HSC 33.333 91.667 1.65

365 KF TF and HSC 33.333 91.667 1.737

366 HSC TF and KF 33.333 91.667 1.737 367 KF BQF and HSC 33.333 91.667 1.737

368 HSC BQF and KF 33.333 91.667 1.737 369 SBF BQF and CCF 33.333 91.667 1.65

370 CCF BQF and SBF 33.333 91.667 1.65 371 KF BQF and CCF 33.333 91.667 1.737

372 CCF BQF and KF 33.333 91.667 1.65

128

373 KF BQF and SBF 33.333 91.667 1.737 374 SBF BQF and KF 33.333 91.667 1.65

375 CBF PBS and SBF 30.556 90.909 1.818 376 CBF PBS and KF 30.556 90.909 1.818

377 CBF HSS and KF 30.556 90.909 1.818

378 PSF Yolk and KF 30.556 90.909 2.727 379 KF PSF and CBQ 30.556 90.909 1.722

380 KF PSF and CKF 30.556 90.909 1.722 381 KF PSF and HSC 30.556 90.909 1.722

382 CVF Yolk and KF 30.556 90.909 2.727

383 KF CVF and CBQ 30.556 90.909 1.722 384 KF CVF and CKF 30.556 90.909 1.722

385 KF CVF and HSC 30.556 90.909 1.722 386 KF PBS and SBF 30.556 90.909 1.722

387 SBF PBS and KF 30.556 90.909 1.636 388 SBF HSS and KF 30.556 90.909 1.636

389 SBF CF and HSC 30.556 90.909 1.636

390 Yolk CBQ and CCF 30.556 90.909 2.517 391 Yolk CBQ and SBF 30.556 90.909 2.517

392 KF Yolk and HSC 30.556 90.909 1.722 393 HSC Yolk and KF 30.556 90.909 1.722

394 CCF Yolk and KF 30.556 90.909 1.636

395 SBF Yolk and KF 30.556 90.909 1.636 396 CBQ CKF and TF 30.556 90.909 2.517

397 CBQ TF and BQF 30.556 90.909 2.517 398 HSC CBQ and CCF 30.556 90.909 1.722

399 HSC CBQ and SBF 30.556 90.909 1.722 400 SBF CBQ and CCF 30.556 90.909 1.636

401 CCF CBQ and SBF 30.556 90.909 1.636

402 SBF CKF and TF 30.556 90.909 1.636 403 SBF TF and BQF 30.556 90.909 1.636

404 CBF PBS and HSC 27.778 90 1.8 405 CBF HSS and HSC 27.778 90 1.8

406 SBF CBF and KF 27.778 90 1.62

407 PSF Yolk and CCF 27.778 90 2.7 408 PSF Yolk and SBF 27.778 90 2.7

409 CCF PSF and KF 27.778 90 1.62 410 SBF PSF and KF 27.778 90 1.62

411 CVF Yolk and CCF 27.778 90 2.7 412 CVF Yolk and SBF 27.778 90 2.7

413 CCF CVF and KF 27.778 90 1.62

129

414 SBF CVF and KF 27.778 90 1.62 415 HSC CF and CCF 27.778 90 1.705

416 SBF CF and CCF 27.778 90 1.62 417 Yolk CBQ and TF 27.778 90 2.492

418 HSC Yolk and CCF 27.778 90 1.705

419 HSC Yolk and SBF 27.778 90 1.705 420 SBF Yolk and CCF 27.778 90 1.62

421 CCF Yolk and SBF 27.778 90 1.62 422 HSC CBQ and TF 27.778 90 1.705

423 SBF CBQ and TF 27.778 90 1.62

424 KF CBQ and TF 27.778 90 1.705 Table 11 Scoring for All Rule Sets (Mid China Region)



ID Probability Rule



4 1 if Pepper beef steak = 1 and Cumin BBQ = 1 then 1.000


5 1 if Pepper beef steak = 1 and BBQ flavor = 1 then 1.000

4 1 if spices salt = 1 and Cumin BBQ = 1 then 1.000





ID Probability Rule

10 1 if Crab flavor = 1 and Cheese corn flavor = 1 then 1.000








12 1 if Chives flavor = 1 then 1.000




12 1 if Chives flavor = 1 and Yolk = 1 then 1.000

12 1 if Chives flavor = 1 and BBQ flavor = 1 then 1.000

130






12 0.923 if BBQ flavor = 1 and Kimchi flavor = 1 then 1.000

13 0.923 if Cumin BBQ = 1 then 1.000



7 0.917 if Pork steak = 1 and Crab flavor = 1 then 1.000



8 0.917 if Pork steak = 1 and Tomato flavor = 1 then 1.000

9 0.917 if Pork steak = 1 and Cheese corn flavor = 1 then 1.000

9 0.917 if Pork steak = 1 and Sauced beef flavor = 1 then 1.000

10 0.917 if Pork steak = 1 and Kimchi flavor = 1 then 1.000

7 0.917 if Chives flavor = 1 and Crab flavor = 1 then 1.000

11 0.917 if Chives flavor = 1 and Cumin BBQ = 1 then 1.000

11 0.917 if Chives flavor = 1 and Chicken flavor = 1 then 1.000

8 0.917 if Chives flavor = 1 and Tomato flavor = 1 then 1.000

9 0.917 if Chives flavor = 1 and Cheese corn flavor = 1 then 1.000


10 0.909 if Chives flavor = 1 and Kimchi flavor = 1 then 1.000

7 0.909 if Crab flavor = 1 and Yolk = 1 then 1.000







ID Probability Rule











131


7 1 if Crab flavor = 1 and Yolk = 1 then 1.000


13 0.923 if Yolk = 1 then 1.000






12 0.917 if BBQ flavor = 1 and Hot sichuan = 1 then 1.000






ID Probability Rule

7 1 if Curry beef = 1 and Crab flavor = 1 then 1.000




7 1 if Chives flavor = 1 and Crab flavor = 1 then 1.000


9 1 if Chives flavor = 1 and Sauced beef flavor = 1 then 1.000

8 1 if Pepper beef steak = 1 and Crab flavor = 1 then 1.000


8 1 if spices salt = 1 and Crab flavor = 1 then 1.000




8 1 if Crab flavor = 1 and Chicken flavor = 1 then 1.000

9 1 if Crab flavor = 1 and Tomato flavor = 1 then 1.000

8 1 if Crab flavor = 1 and BBQ flavor = 1 then 1.000

11 1 if Crab flavor = 1 and Hot sichuan = 1 then 1.000







14 1 if Hot sichuan = 1 and Sauced beef flavor = 1 then 1.000

15 0.933 if Hot sichuan = 1 and Cheese corn flavor = 1 then 1.000

132

15 0.933 if Cheese corn flavor = 1 and Sauced beef flavor = 1 then 1.000

14 0.929 if Crab flavor = 1 then 1.000

13 0.923 if Cumin BBQ = 1 then 1.000



13 0.923 if Tomato flavor = 1 and Sauced beef flavor = 1 then 1.000









12 0.917 if BBQ flavor = 1 and Cheese corn flavor = 1 then 1.000

12 0.917 if BBQ flavor = 1 and Sauced beef flavor = 1 then 1.000






11 0.909 if Chives flavor = 1 and Hot sichuan = 1 then 1.000





1.000 = Yolk Flavor

ID Probability Rule














133

11 1 if Chives flavor = 1 and Cumin BBQ = 1 then 1.000

11 1 if Chives flavor = 1 and Chicken flavor = 1 then 1.000

8 1 if Chives flavor = 1 and Tomato flavor = 1 then 1.000


11 1 if Chives flavor = 1 and Hot sichuan = 1 then 1.000



10 1 if Chives flavor = 1 and Kimchi flavor = 1 then 1.000

13 0.923 if Cumin BBQ = 1 then 1.000














ID Probability Rule














13 0.923 if Yolk = 1 then 1.000





134








ID Probability Rule

17 1 if Pepper beef steak = 1 then 1.000

16 1 if Curry beef = 1 and Pepper beef steak = 1 then 1.000








8 1 if Pepper beef steak = 1 and Crab flavor = 1 then 1.000



9 1 if Pepper beef steak = 1 and Tomato flavor = 1 then 1.000


10 1 if Pepper beef steak = 1 and Hot sichuan = 1 then 1.000

12 1 if Pepper beef steak = 1 and Cheese corn flavor = 1 then 1.000



18 0.944 if Curry beef = 1 then 1.000




ID Probability Rule











135



12 1 if BBQ flavor = 1 and Sauced beef flavor = 1 then 1.000

13 1 if Yolk = 1 then 1.000






















9 0.917 if Yolk = 1 and Tomato flavor = 1 then 1.000











ID Probability Rule


17 0.941 if Pepper beef steak = 1 and spices salt = 1 then 1.000



136





10 0.9 if Pepper beef steak = 1 and Hot sichuan = 1 then 1.000

10 0.9 if spices salt = 1 and Hot sichuan = 1 then 1.000



ID Probability Rule































13 1 if Cumin BBQ = 1 and BBQ flavor = 1 then 1.000


137





12 1 if BBQ flavor = 1 and Hot sichuan = 1 then 1.000


12 1 if BBQ flavor = 1 and Sauced beef flavor = 1 then 1.000


15 0.933 if BBQ flavor = 1 then 1.000

13 0.923 if Yolk = 1 then 1.000



12 0.917 if Chives flavor = 1 then 1.000




12 0.917 if Chives flavor = 1 and Yolk = 1 then 1.000




ID Probability Rule








8 1 if spices salt = 1 and Crab flavor = 1 then 1.000








17 0.941 if Curry beef = 1 and spices salt = 1 then 1.000


BBQ Flavor Rule Set

1.000 = BBQ Flavor

ID Probability Rule

138























13 1 if Yolk = 1 then 1.000












13 1 if Cumin BBQ = 1 and Chicken flavor = 1 then 1.000







139












ID Probability Rule












12 0.917 if BBQ flavor = 1 and Sauced beef flavor = 1 then 1.000









ID Probability Rule









140


9 1 if Crab flavor = 1 and Tomato flavor = 1 then 1.000







13 0.923 if Crab flavor = 1 and Kimchi flavor = 1 then 1.000

















10 0.9 if Crab flavor = 1 and Cheese corn flavor = 1 then 1.000


10 0.9 if Cumin BBQ = 1 and Tomato flavor = 1 then 1.000 Table 12 Scoring information for Top 10 Rules in Mid China Region Year 2013 2014 2015 2014 2015 2013 2015 2013 2014 2013

Month Jan May Sep Feb Jun Nov Mar Aug Dec May TF 0 0 1 0 0 0 1 1 1 0

CF 0 0 1 0 0 1 0 0 1 0

SBF 0 0 1 0 0 1 1 1 1 0 CCF 0 0 1 0 1 1 1 1 1 0

BQF 0 0 1 0 1 0 1 1 0 0 PSB 1 0 1 0 0 1 0 1 1 0

CKF 0 0 1 0 1 0 1 1 0 0 CF 1 0 1 0 0 1 0 1 1 0

CBQ 0 0 1 0 1 0 1 0 0 0

141

HSS 1 1 1 0 0 1 0 1 1 0 PSF 0 0 1 0 1 0 1 0 0 0

YF 0 0 1 0 1 0 1 0 0 0 KF 0 0 1 0 1 1 1 0 1 0

CVF 0 0 1 0 1 0 1 0 0 0

HSC 0 0 1 0 1 0 1 0 1 0 Prediction 1 SBF HSC KF

Condition 1 0.917 0.9 0.933 Rule ID 1 320 409 278

Prediction 2 CBQ Condition 2 0.929

Rule ID 2 281

Prediction 3 Condition 3

Rule ID 3 Table 13 Results for the East China Region


1 CVF PSK 33.333 100 2.571

2 CKF PSK 33.333 100 2.571

3 CBQ PSK 33.333 100 2.571 4 BQF PSK 33.333 100 2.4

5 HSC PSK 33.333 100 1.895 6 BQF CBQ 38.889 100 2.4

7 PBS CBF 52.778 100 1.714 8 CVF PSK and CF 11.111 100 2.571

9 CKF PSK and CF 11.111 100 2.571

10 CBQ PSK and CF 11.111 100 2.571 11 TF PSK and CF 11.111 100 1.8

12 CF PSK and TF 11.111 100 2.25 13 BQF PSK and CF 11.111 100 2.4

14 SBF PSK and CF 11.111 100 1.895

15 HSC PSK and CF 11.111 100 1.895 16 CCF PSK and CF 11.111 100 2

17 KF PSK and CF 11.111 100 1.895 18 CVF PSK and Yolk 30.556 100 2.571

19 CKF PSK and Yolk 30.556 100 2.571 20 CBQ PSK and Yolk 30.556 100 2.571

21 BQF PSK and Yolk 30.556 100 2.4

22 HSC PSK and Yolk 30.556 100 1.895 23 PSK Yolk and HSC 30.556 100 3

142

24 CKF PSK and CVF 33.333 100 2.571 25 CVF PSK and CKF 33.333 100 2.571

26 CBQ PSK and CVF 33.333 100 2.571 27 CVF PSK and CBQ 33.333 100 2.571

28 CVF PSK and TF 11.111 100 2.571

29 BQF PSK and CVF 33.333 100 2.4 30 CVF PSK and BQF 33.333 100 2.571

31 CVF PSK and PBS 11.111 100 2.571 32 CVF PSK and SBF 16.667 100 2.571

33 HSC PSK and CVF 33.333 100 1.895

34 CVF PSK and HSC 33.333 100 2.571 35 CVF PSK and CCF 27.778 100 2.571

36 CVF PSK and KF 30.556 100 2.571 37 CBQ PSK and CKF 33.333 100 2.571

38 CKF PSK and CBQ 33.333 100 2.571 39 CKF PSK and TF 11.111 100 2.571

40 BQF PSK and CKF 33.333 100 2.4

41 CKF PSK and BQF 33.333 100 2.571 42 CKF PSK and PBS 11.111 100 2.571

43 CKF PSK and SBF 16.667 100 2.571 44 HSC PSK and CKF 33.333 100 1.895

45 CKF PSK and HSC 33.333 100 2.571

46 CKF PSK and CCF 27.778 100 2.571 47 CKF PSK and KF 30.556 100 2.571

48 CBQ PSK and TF 11.111 100 2.571 49 BQF PSK and CBQ 33.333 100 2.4

50 CBQ PSK and BQF 33.333 100 2.571 51 CBQ PSK and PBS 11.111 100 2.571

52 CBQ PSK and SBF 16.667 100 2.571

53 HSC PSK and CBQ 33.333 100 1.895 54 CBQ PSK and HSC 33.333 100 2.571

55 PSK CBQ and HSC 33.333 100 3 56 CBQ PSK and CCF 27.778 100 2.571

57 CBQ PSK and KF 30.556 100 2.571

58 BQF PSK and TF 11.111 100 2.4 59 SBF PSK and TF 11.111 100 1.895

60 HSC PSK and TF 11.111 100 1.895 61 CCF PSK and TF 11.111 100 2

62 KF PSK and TF 11.111 100 1.895 63 BQF PSK and PBS 11.111 100 2.4

64 BQF PSK and SBF 16.667 100 2.4

143

65 HSC PSK and BQF 33.333 100 1.895 66 BQF PSK and HSC 33.333 100 2.4

67 BQF PSK and CCF 27.778 100 2.4 68 BQF PSK and KF 30.556 100 2.4

69 SBF PSK and PBS 11.111 100 1.895

70 HSC PSK and PBS 11.111 100 1.895 71 CCF PSK and PBS 11.111 100 2

72 KF PSK and PBS 11.111 100 1.895 73 HSC PSK and SBF 16.667 100 1.895

74 CCF PSK and SBF 16.667 100 2

75 KF PSK and SBF 16.667 100 1.895 76 HSC PSK and CCF 27.778 100 1.895

77 HSC PSK and KF 30.556 100 1.895 78 KF PSK and CCF 27.778 100 1.895

79 TF CF and Yolk 13.889 100 1.8 80 SBF CF and Yolk 13.889 100 1.895

81 KF CF and Yolk 13.889 100 1.895

82 SBF CF and HSS 27.778 100 1.895 83 CKF CF and CVF 13.889 100 2.571

84 CBQ CF and CVF 13.889 100 2.571 85 CVF CF and CBQ 13.889 100 2.571

86 TF CF and CVF 13.889 100 1.8

87 CF CVF and TF 13.889 100 2.25 88 BQF CF and CVF 13.889 100 2.4

89 SBF CF and CVF 13.889 100 1.895 90 CCF CF and CVF 13.889 100 2

91 KF CF and CVF 13.889 100 1.895 92 CKF CF and CBQ 13.889 100 2.571

93 CF CKF and TF 13.889 100 2.25

94 CF CKF and CBF 13.889 100 2.25 95 SBF CF and CKF 16.667 100 1.895

96 CCF CF and CKF 16.667 100 2 97 KF CF and CKF 16.667 100 1.895

98 TF CF and CBQ 13.889 100 1.8

99 BQF CF and CBQ 13.889 100 2.4 100 SBF CF and CBQ 13.889 100 1.895

101 CCF CF and CBQ 13.889 100 2 102 KF CF and CBQ 13.889 100 1.895

103 TF CF and BQF 16.667 100 1.8 104 PBS CF and CBF 30.556 100 1.714

105 SBF CF and BQF 16.667 100 1.895

144

106 CCF CF and BQF 16.667 100 2 107 KF CF and BQF 16.667 100 1.895

108 SBF CF and CCF 25 100 1.895 109 SBF CF and KF 27.778 100 1.895

110 CKF Yolk and CVF 33.333 100 2.571

111 CVF Yolk and CKF 33.333 100 2.571 112 CBQ Yolk and CVF 33.333 100 2.571

113 BQF Yolk and CVF 33.333 100 2.4 114 CVF Yolk and HSC 30.556 100 2.571

115 CBQ Yolk and CKF 33.333 100 2.571

116 BQF Yolk and CKF 33.333 100 2.4 117 CKF Yolk and HSC 30.556 100 2.571

118 BQF Yolk and CBQ 36.111 100 2.4 119 CBQ Yolk and BQF 36.111 100 2.571

120 CBQ Yolk and HSC 30.556 100 2.571 121 CBQ Yolk and CCF 30.556 100 2.571

122 TF Yolk and CBF 13.889 100 1.8

123 SBF Yolk and TF 16.667 100 1.895 124 PBS Yolk and CBF 13.889 100 1.714

125 SBF Yolk and CBF 13.889 100 1.895 126 BQF Yolk and HSC 30.556 100 2.4

127 BQF Yolk and CCF 30.556 100 2.4

128 SBF Yolk and PBS 16.667 100 1.895 129 CCF HSS and CVF 13.889 100 2

130 KF HSS and CVF 13.889 100 1.895 131 CCF HSS and CKF 13.889 100 2

132 KF HSS and CKF 13.889 100 1.895 133 BQF HSS and CBQ 13.889 100 2.4

134 CCF HSS and CBQ 13.889 100 2

135 PBS HSS and CBF 44.444 100 1.714 136 CBF HSS and PBS 44.444 100 1.895

137 CCF HSS and BQF 16.667 100 2 138 CBQ CVF and CKF 36.111 100 2.571

139 CKF CVF and CBQ 36.111 100 2.571

140 CVF CKF and CBQ 36.111 100 2.571 141 CKF CVF and TF 13.889 100 2.571

142 CVF CKF and TF 13.889 100 2.571 143 BQF CVF and CKF 36.111 100 2.4

144 CKF CVF and BQF 36.111 100 2.571 145 CVF CKF and BQF 36.111 100 2.571

146 CBQ CVF and TF 13.889 100 2.571

145

147 BQF CVF and CBQ 36.111 100 2.4 148 CBQ CVF and BQF 36.111 100 2.571

149 CVF CBQ and HSC 33.333 100 2.571 150 CVF CBQ and KF 33.333 100 2.571

151 BQF CVF and TF 13.889 100 2.4

152 SBF CVF and TF 13.889 100 1.895 153 CCF CVF and TF 13.889 100 2

154 KF CVF and TF 13.889 100 1.895 155 PBS CVF and CBF 13.889 100 1.714

156 SBF CVF and CBF 13.889 100 1.895

157 CCF CVF and CBF 13.889 100 2 158 KF CVF and CBF 13.889 100 1.895

159 SBF CVF and PBS 16.667 100 1.895 160 CCF CVF and PBS 16.667 100 2

161 KF CVF and PBS 16.667 100 1.895 162 CCF CVF and SBF 22.222 100 2

163 KF CVF and SBF 22.222 100 1.895

164 KF CVF and CCF 33.333 100 1.895 165 CBQ CKF and TF 13.889 100 2.571

166 BQF CKF and CBQ 36.111 100 2.4 167 CBQ CKF and BQF 36.111 100 2.571

168 CKF CBQ and HSC 33.333 100 2.571

169 CKF CBQ and KF 33.333 100 2.571 170 BQF CKF and TF 13.889 100 2.4

171 SBF CKF and TF 13.889 100 1.895 172 CCF CKF and TF 13.889 100 2

173 KF CKF and TF 13.889 100 1.895 174 PBS CKF and CBF 13.889 100 1.714

175 SBF CKF and CBF 13.889 100 1.895

176 CCF CKF and CBF 13.889 100 2 177 KF CKF and CBF 13.889 100 1.895

178 SBF CKF and PBS 16.667 100 1.895 179 CCF CKF and PBS 16.667 100 2

180 KF CKF and PBS 16.667 100 1.895

181 CCF CKF and SBF 22.222 100 2 182 KF CKF and SBF 22.222 100 1.895

183 KF CKF and CCF 33.333 100 1.895 184 TF CBQ and CBF 13.889 100 1.8

185 BQF CBQ and TF 16.667 100 2.4 186 SBF CBQ and TF 16.667 100 1.895

187 CCF CBQ and TF 16.667 100 2

146

188 BQF CBQ and CBF 13.889 100 2.4 189 PBS CBQ and CBF 13.889 100 1.714

190 SBF CBQ and CBF 13.889 100 1.895 191 CCF CBQ and CBF 13.889 100 2

192 BQF CBQ and PBS 16.667 100 2.4

193 BQF CBQ and SBF 22.222 100 2.4 194 BQF CBQ and HSC 33.333 100 2.4

195 BQF CBQ and CCF 33.333 100 2.4 196 BQF CBQ and KF 33.333 100 2.4

197 SBF CBQ and PBS 16.667 100 1.895

198 CCF CBQ and PBS 16.667 100 2 199 CCF CBQ and SBF 22.222 100 2

200 TF CBF and BQF 16.667 100 1.8 201 PBS TF and CBF 41.667 100 1.714

202 CBF TF and PBS 41.667 100 1.895 203 SBF TF and BQF 19.444 100 1.895

204 CCF TF and BQF 19.444 100 2

205 SBF TF and CCF 25 100 1.895 206 PBS CBF and BQF 16.667 100 1.714

207 SBF CBF and BQF 16.667 100 1.895 208 CCF CBF and BQF 16.667 100 2

209 PBS CBF and SBF 36.111 100 1.714

210 PBS CBF and HSC 25 100 1.714 211 PBS CBF and CCF 30.556 100 1.714

212 PBS CBF and KF 30.556 100 1.714 213 SBF BQF and PBS 19.444 100 1.895

214 CCF BQF and PBS 19.444 100 2 215 CCF BQF and SBF 25 100 2

216 CCF SBF and HSC 30.556 100 2

217 CBQ BQF 41.667 93.333 2.4 218 KF HSC and CCF 41.667 93.333 1.768

219 HSC CCF and KF 41.667 93.333 1.768 220 CBQ Yolk 38.889 92.857 2.388

221 Yolk CBQ 38.889 92.857 2.388

222 BQF Yolk 38.889 92.857 2.229 223 CKF CVF 38.889 92.857 2.388

224 CVF CKF 38.889 92.857 2.388 225 CBQ CVF 38.889 92.857 2.388

226 CVF CBQ 38.889 92.857 2.388 227 BQF CVF 38.889 92.857 2.229

228 HSC CVF 38.889 92.857 1.759

147

229 KF CVF 38.889 92.857 1.759 230 CBQ CKF 38.889 92.857 2.388

231 CKF CBQ 38.889 92.857 2.388 232 BQF CKF 38.889 92.857 2.229

233 HSC CKF 38.889 92.857 1.759

234 KF CKF 38.889 92.857 1.759 235 SBF CF and TF 38.889 92.857 1.759

236 TF CF and SBF 38.889 92.857 1.671 237 CF TF and SBF 38.889 92.857 2.089

238 Yolk CBQ and BQF 38.889 92.857 2.388

239 CBF HSS and TF 38.889 92.857 1.759 240 PBS HSS and TF 38.889 92.857 1.592

241 CVF CBQ and BQF 38.889 92.857 2.388 242 CKF CBQ and BQF 38.889 92.857 2.388

243 CBF PBS and SBF 38.889 92.857 1.759 244 PSK CVF and CKF 36.111 92.308 2.769

245 PSK CVF and CBQ 36.111 92.308 2.769

246 PSK CVF and BQF 36.111 92.308 2.769 247 PSK CVF and HSC 36.111 92.308 2.769

248 PSK CKF and CBQ 36.111 92.308 2.769 249 PSK CKF and BQF 36.111 92.308 2.769

250 PSK CKF and HSC 36.111 92.308 2.769

251 PSK BQF and HSC 36.111 92.308 2.769 252 Yolk CVF and CKF 36.111 92.308 2.374

253 CVF Yolk and CBQ 36.111 92.308 2.374 254 Yolk CVF and CBQ 36.111 92.308 2.374

255 CVF Yolk and BQF 36.111 92.308 2.374 256 Yolk CVF and BQF 36.111 92.308 2.374

257 CKF Yolk and CBQ 36.111 92.308 2.374

258 Yolk CKF and CBQ 36.111 92.308 2.374 259 CKF Yolk and BQF 36.111 92.308 2.374

260 Yolk CKF and BQF 36.111 92.308 2.374 261 HSC CVF and CKF 36.111 92.308 1.749

262 CKF CVF and HSC 36.111 92.308 2.374

263 CVF CKF and HSC 36.111 92.308 2.374 264 KF CVF and CKF 36.111 92.308 1.749

265 CKF CVF and KF 36.111 92.308 2.374 266 CVF CKF and KF 36.111 92.308 2.374

267 HSC CVF and CBQ 36.111 92.308 1.749 268 CBQ CVF and HSC 36.111 92.308 2.374

269 KF CVF and CBQ 36.111 92.308 1.749

148

270 CBQ CVF and KF 36.111 92.308 2.374 271 HSC CVF and BQF 36.111 92.308 1.749

272 BQF CVF and HSC 36.111 92.308 2.215 273 CVF BQF and HSC 36.111 92.308 2.374

274 KF CVF and BQF 36.111 92.308 1.749

275 BQF CVF and KF 36.111 92.308 2.215 276 CVF BQF and KF 36.111 92.308 2.374

277 KF CVF and HSC 36.111 92.308 1.749 278 HSC CVF and KF 36.111 92.308 1.749

279 CCF CVF and KF 36.111 92.308 1.846

280 HSC CKF and CBQ 36.111 92.308 1.749 281 CBQ CKF and HSC 36.111 92.308 2.374

282 KF CKF and CBQ 36.111 92.308 1.749 283 CBQ CKF and KF 36.111 92.308 2.374

284 HSC CKF and BQF 36.111 92.308 1.749 285 BQF CKF and HSC 36.111 92.308 2.215

286 CKF BQF and HSC 36.111 92.308 2.374

287 KF CKF and BQF 36.111 92.308 1.749 288 BQF CKF and KF 36.111 92.308 2.215

289 CKF BQF and KF 36.111 92.308 2.374 290 KF CKF and HSC 36.111 92.308 1.749

291 HSC CKF and KF 36.111 92.308 1.749

292 CCF CKF and KF 36.111 92.308 1.846 293 CBQ BQF and HSC 36.111 92.308 2.374

294 CBQ BQF and CCF 36.111 92.308 2.374 295 CBQ BQF and KF 36.111 92.308 2.374

296 KF BQF and HSC 36.111 92.308 1.749 297 HSC BQF and KF 36.111 92.308 1.749

298 KF BQF and CCF 36.111 92.308 1.749

299 CCF BQF and KF 36.111 92.308 1.846 300 Yolk PSK 33.333 91.667 2.357

301 KF PSK 33.333 91.667 1.737 302 Yolk PSK and CVF 33.333 91.667 2.357

303 PSK Yolk and CVF 33.333 91.667 2.75

304 Yolk PSK and CKF 33.333 91.667 2.357 305 PSK Yolk and CKF 33.333 91.667 2.75

306 Yolk PSK and CBQ 33.333 91.667 2.357 307 Yolk PSK and BQF 33.333 91.667 2.357

308 Yolk PSK and HSC 33.333 91.667 2.357 309 KF PSK and CVF 33.333 91.667 1.737

310 KF PSK and CKF 33.333 91.667 1.737

149

311 KF PSK and CBQ 33.333 91.667 1.737 312 PSK CBQ and KF 33.333 91.667 2.75

313 KF PSK and BQF 33.333 91.667 1.737 314 KF PSK and HSC 33.333 91.667 1.737

315 CBF CF and PBS 33.333 91.667 1.737

316 HSC Yolk and CVF 33.333 91.667 1.737 317 KF Yolk and CVF 33.333 91.667 1.737

318 CVF Yolk and KF 33.333 91.667 2.357 319 HSC Yolk and CKF 33.333 91.667 1.737

320 KF Yolk and CKF 33.333 91.667 1.737

321 CKF Yolk and KF 33.333 91.667 2.357 322 Yolk CBQ and HSC 33.333 91.667 2.357

323 Yolk CBQ and CCF 33.333 91.667 2.357 324 CBQ Yolk and KF 33.333 91.667 2.357

325 Yolk CBQ and KF 33.333 91.667 2.357 326 BQF Yolk and KF 33.333 91.667 2.2

327 CBF HSS and SBF 33.333 91.667 1.737

328 PBS HSS and SBF 33.333 91.667 1.571 329 CKF CVF and CCF 33.333 91.667 2.357

330 CVF CKF and CCF 33.333 91.667 2.357 331 CBQ CVF and CCF 33.333 91.667 2.357

332 CVF CBQ and CCF 33.333 91.667 2.357

333 BQF CVF and CCF 33.333 91.667 2.2 334 HSC CVF and CCF 33.333 91.667 1.737

335 CBQ CKF and CCF 33.333 91.667 2.357 336 CKF CBQ and CCF 33.333 91.667 2.357

337 BQF CKF and CCF 33.333 91.667 2.2 338 HSC CKF and CCF 33.333 91.667 1.737

339 KF CBQ and HSC 33.333 91.667 1.737

340 HSC CBQ and KF 33.333 91.667 1.737 341 KF CBQ and CCF 33.333 91.667 1.737

342 CCF CBQ and KF 33.333 91.667 1.833 343 CBF PBS and CCF 33.333 91.667 1.737

344 CBF PBS and KF 33.333 91.667 1.737

345 SBF PBS and CCF 33.333 91.667 1.737 346 SBF PBS and KF 33.333 91.667 1.737

347 KF PSK and Yolk 30.556 90.909 1.722 348 Yolk PSK and KF 30.556 90.909 2.338

349 CCF PSK and KF 30.556 90.909 1.818 350 TF CF and CBF 30.556 90.909 1.636

351 SBF CF and CBF 30.556 90.909 1.722

150

352 CVF Yolk and CCF 30.556 90.909 2.338 353 CKF Yolk and CCF 30.556 90.909 2.338

354 KF Yolk and HSC 30.556 90.909 1.722 355 KF Yolk and CCF 30.556 90.909 1.722

356 CBF HSS and CCF 30.556 90.909 1.722

357 HSS CBF and CCF 30.556 90.909 1.722 358 CBF HSS and KF 30.556 90.909 1.722

359 HSS CBF and KF 30.556 90.909 1.722 360 PBS HSS and CCF 30.556 90.909 1.558

361 PBS HSS and KF 30.556 90.909 1.558

362 SBF CBF and CCF 30.556 90.909 1.722 363 SBF CBF and KF 30.556 90.909 1.722

364 KF SBF and HSC 30.556 90.909 1.722 365 CBF PBS 58.333 90.476 1.714

366 Yolk PSK and CCF 27.778 90 2.314 367 TF CF and HSS 27.778 90 1.62

368 CBF CF and HSS 27.778 90 1.705

369 PBS CF and HSS 27.778 90 1.543 370 TF CF and KF 27.778 90 1.62

371 CF TF and KF 27.778 90 2.025 372 CBF CF and KF 27.778 90 1.705

373 PBS CF and KF 27.778 90 1.543

374 CBF TF and KF 27.778 90 1.705 375 PBS TF and KF 27.778 90 1.543

376 SBF TF and KF 27.778 90 1.705 Table 14 Scoring for All Rule Sets (East China Region)



ID Probability Rule










4 1 if Pork steak = 1 and Pepper beef steak = 1 then 1.000



151

15 0.933 if Cheese corn flavor = 1 and Kimchi flavor = 1 then 1.000






13 0.923 if Chicken flavor = 1 and Cumin BBQ = 1 then 1.000












ID Probability Rule




1.000 = Yolk Flavor

ID Probability Rule

14 0.929 if Cumin BBQ = 1 then 1.000

















152




ID Probability Rule













5 1 if Chicken flavor = 1 and Curry beef = 1 then 1.000




6 1 if BBQ flavor = 1 and Hot sichuan = 1 then 1.000


5 1 if Sauced beef flavor = 1 and Hot sichuan = 1 then 1.000

10 1 if Hot sichuan = 1 and Cheese corn flavor = 1 then 1.000






13 0.923 if Crab flavor = 1 and Chives flavor = 1 then 1.000



13 0.923 if spices salt = 1 and Chives flavor = 1 then 1.000


13 0.923 if Chives flavor = 1 and Curry beef = 1 then 1.000

13 0.923 if Chives flavor = 1 and Pepper beef steak = 1 then 1.000




12 0.917 if Pork steak = 1 and Pepper beef steak = 1 then 1.000

12 0.917 if Pork steak = 1 and Sauced beef flavor = 1 then 1.000

153







12 0.917 if Pork steak = 1 and Tomato flavor = 1 then 1.000



11 0.909 if Chicken flavor = 1 and Pepper beef steak = 1 then 1.000

11 0.909 if Pork steak = 1 and Crab flavor = 1 then 1.000


1.000 = Crab Flavor

ID Probability Rule






10 0.9 if Tomato flavor = 1 and Kimchi flavor = 1 then 1.000



ID Probability Rule



















154






14 0.929 if Cumin BBQ = 1 then 1.000













ID Probability Rule

16 1 if spices salt = 1 and Curry beef = 1 then 1.000

5 1 if Yolk = 1 and Curry beef = 1 then 1.000

11 1 if Crab flavor = 1 and Curry beef = 1 then 1.000




13 1 if Curry beef = 1 and Sauced beef flavor = 1 then 1.000


15 1 if Tomato flavor = 1 and Curry beef = 1 then 1.000

5 1 if Cumin BBQ = 1 and Curry beef = 1 then 1.000


5 1 if Chives flavor = 1 and Curry beef = 1 then 1.000

19 1 if Curry beef = 1 then 1.000






10 0.9 if Crab flavor = 1 and spices salt = 1 then 1.000




155

ID Probability Rule
















ID Probability Rule









1 0.929 if Crab flavor = 1 and Sauced beef flavor = 1 then 1.000

1 0.909 if Crab flavor = 1 and Curry beef = 1 then 1.000





ID Probability Rule










156

14 1 if Chicken flavor = 1 and Cumin BBQ = 1 then 1.000













14 0.929 if Cumin BBQ = 1 then 1.000













ID Probability Rule




10 1 if Crab flavor = 1 and spices salt = 1 then 1.000


7 1 if BBQ flavor = 1 and Pepper beef steak = 1 then 1.000








6 1 if Chicken flavor = 1 and Pepper beef steak = 1 then 1.000

157




6 1 if Chives flavor = 1 and Pepper beef steak = 1 then 1.000



6 1 if Yolk = 1 and Pepper beef steak = 1 then 1.000



10 1 if Crab flavor = 1 and Kimchi flavor = 1 then 1.000









11 0.909 if Crab flavor = 1 and Curry beef = 1 then 1.000


BBQ Flavor Rule Set

1.000 = BBQ Flavor

ID Probability Rule



















158











14 0.929 if Yolk = 1 then 1.000












ID Probability Rule



















159







14 0.929 if Yolk = 1 then 1.000















ID Probability Rule

15 1 if Tomato flavor = 1 and Pepper beef steak = 1 then 1.000

16 1 if spices salt = 1 and Pepper beef steak = 1 then 1.000















ID Probability Rule



160


















6 1 if Chives flavor = 1 and Pepper beef steak = 1 then 1.000












7 0.917 if BBQ flavor = 1 and Pepper beef steak = 1 then 1.000

9 0.909 if BBQ flavor = 1 and Sauced beef flavor = 1 then 1.000 Table 15 Scoring information for Top 10 Rules in East China Region Year 2013 2014 2015 2014 2015 2013 2015 2013 2014 2013

Month Mon May Sep Feb Jun Nov Mar Aug Dec May Tomato flavor 1 1 1 1 0 1 0 1 0 1

Crab flavor 0 1 1 0 0 1 0 0 0 1

Sauced beef 0 1 1 0 1 1 0 1 1 1 Cheese corn 0 0 1 0 1 0 1 1 1 0

BBQ flavor 0 0 1 0 1 0 1 1 0 0 Pepper beef 1 0 1 0 0 1 0 1 1 0

161

Chicken flavor 0 0 1 0 1 0 1 0 0 0 Curry beef 1 0 1 0 0 1 0 1 1 0

Cumin BBQ 0 0 1 0 1 0 1 1 0 0 spices salt 1 1 1 0 0 1 0 1 1 0

Pork steak 0 0 1 0 1 0 1 0 0 0

Yolk 0 0 1 0 1 0 1 1 0 0 Kimchi flavor 0 0 1 0 1 1 1 0 1 0

Chives flavor 0 0 1 0 1 0 1 0 1 0 Hot sichuan 0 0 1 0 1 0 1 0 1 0

Prediction 1 PBS CKF BQF

Confidence 1 0.92

9 0.92

9 0.92

9 Rule ID 1 237 225 230

Prediction 2 CBF CVF CKF

Confidence 2 0.92

9 0.92

9 0.92

9 Rule ID 2 238 227 231

Prediction 3 CF CBQ

Confidence 3 0.92

9 0.92

9 Rule ID 3 236 232

Table 16 Results for the South West China Region


1 CKF YF 33.333 100 2.571

2 BQF YF 33.333 100 2.25

3 CBQ YF 33.333 100 2.25

4 CVF YF 33.333 100 2

5 BQF CKF 38.889 100 2.25

6 YF PSF and CF 13.889 100 3

7 CKF PSF and CF 13.889 100 2.571

8 BQF PSF and CF 13.889 100 2.25

9 CBQ PSF and CF 13.889 100 2.25

10 HSC PSF and CF 13.889 100 2

11 CVF PSF and CF 13.889 100 2

12 CKF PSF and YF 30.556 100 2.571

13 YF PSF and CKF 30.556 100 3

14 YF PSF and KF 22.222 100 3

15 YF PSF and TF 13.889 100 3

16 BQF PSF and YF 30.556 100 2.25

17 YF PSF and BQF 30.556 100 3

18 CBQ PSF and YF 30.556 100 2.25

162

19 YF PSF and CBQ 30.556 100 3

20 HSC PSF and YF 30.556 100 2

21 YF PSF and HSC 30.556 100 3

22 PSF YF and HSC 30.556 100 3

23 YF PSF and CCF 22.222 100 3

24 CVF PSF and YF 30.556 100 2

25 YF PSF and CVF 30.556 100 3

26 YF PSF and SBF 27.778 100 3

27 CKF PSF and KF 22.222 100 2.571

28 CKF PSF and TF 13.889 100 2.571

29 BQF PSF and CKF 30.556 100 2.25

30 CKF PSF and BQF 30.556 100 2.571

31 CBQ PSF and CKF 30.556 100 2.25

32 CKF PSF and CBQ 30.556 100 2.571

33 HSC PSF and CKF 30.556 100 2

34 CKF PSF and HSC 30.556 100 2.571

35 CKF PSF and CCF 22.222 100 2.571

36 CVF PSF and CKF 30.556 100 2

37 CKF PSF and CVF 30.556 100 2.571

38 CKF PSF and SBF 27.778 100 2.571

39 KF PSF and TF 13.889 100 2.4

40 BQF PSF and KF 22.222 100 2.25

41 CBQ PSF and KF 22.222 100 2.25

42 HSC PSF and KF 22.222 100 2

43 CVF PSF and KF 22.222 100 2

44 SBF PSF and KF 22.222 100 1.714

45 BQF PSF and TF 13.889 100 2.25

46 CBQ PSF and TF 13.889 100 2.25

47 HSC PSF and TF 13.889 100 2

48 CVF PSF and TF 13.889 100 2

49 SBF PSF and TF 13.889 100 1.714

50 CBQ PSF and BQF 30.556 100 2.25

51 BQF PSF and CBQ 30.556 100 2.25

52 HSC PSF and BQF 30.556 100 2

53 BQF PSF and HSC 30.556 100 2.25

54 BQF PSF and CCF 22.222 100 2.25

55 CVF PSF and BQF 30.556 100 2

56 BQF PSF and CVF 30.556 100 2.25

57 BQF PSF and SBF 27.778 100 2.25

58 HSC PSF and CBQ 30.556 100 2

59 CBQ PSF and HSC 30.556 100 2.25

163

60 CBQ PSF and CCF 22.222 100 2.25

61 CVF PSF and CBQ 30.556 100 2

62 CBQ PSF and CVF 30.556 100 2.25

63 CBQ PSF and SBF 27.778 100 2.25

64 HSC PSF and CCF 22.222 100 2

65 CVF PSF and HSC 30.556 100 2

66 HSC PSF and CVF 30.556 100 2

67 HSC PSF and SBF 27.778 100 2

68 CVF PSF and CCF 22.222 100 2

69 CVF PSF and SBF 27.778 100 2

70 CKF CF and YF 16.667 100 2.571

71 YF CF and CKF 16.667 100 3

72 BQF CF and YF 16.667 100 2.25

73 YF CF and BQF 16.667 100 3

74 CBQ CF and YF 16.667 100 2.25

75 CVF CF and YF 16.667 100 2

76 BQF CF and CKF 16.667 100 2.25

77 CKF CF and BQF 16.667 100 2.571

78 CBQ CF and CKF 16.667 100 2.25

79 CVF CF and CKF 16.667 100 2

80 CBQ CF and BQF 16.667 100 2.25

81 CVF CF and BQF 16.667 100 2

82 CVF CF and CBQ 19.444 100 2

83 CKF YF and KF 25 100 2.571

84 CKF YF and CBF 11.111 100 2.571

85 CKF YF and TF 16.667 100 2.571

86 BQF YF and CKF 33.333 100 2.25

87 CKF YF and BQF 33.333 100 2.571

88 CBQ YF and CKF 33.333 100 2.25

89 CKF YF and CBQ 33.333 100 2.571

90 CKF YF and HSC 30.556 100 2.571

91 CKF YF and CCF 25 100 2.571

92 CVF YF and CKF 33.333 100 2

93 CKF YF and CVF 33.333 100 2.571

94 CKF YF and SBF 30.556 100 2.571

95 KF YF and CBF 11.111 100 2.4

96 KF YF and TF 16.667 100 2.4

97 BQF YF and KF 25 100 2.25

98 CBQ YF and KF 25 100 2.25

99 CVF YF and KF 25 100 2

100 SBF YF and KF 25 100 1.714

164

101 TF YF and CBF 11.111 100 1.895

102 BQF YF and CBF 11.111 100 2.25

103 CBQ YF and CBF 11.111 100 2.25

104 CCF YF and CBF 11.111 100 1.895

105 CVF YF and CBF 11.111 100 2

106 SBF YF and CBF 11.111 100 1.714

107 BQF YF and TF 16.667 100 2.25

108 CBQ YF and TF 16.667 100 2.25

109 CVF YF and TF 16.667 100 2

110 SBF YF and TF 16.667 100 1.714

111 CBQ YF and BQF 33.333 100 2.25

112 BQF YF and CBQ 33.333 100 2.25

113 BQF YF and HSC 30.556 100 2.25

114 BQF YF and CCF 25 100 2.25

115 CVF YF and BQF 33.333 100 2

116 BQF YF and CVF 33.333 100 2.25

117 BQF YF and SBF 30.556 100 2.25

118 CBQ YF and HSC 30.556 100 2.25

119 CBQ YF and CCF 25 100 2.25

120 CVF YF and CBQ 33.333 100 2

121 CBQ YF and CVF 33.333 100 2.25

122 CBQ YF and SBF 30.556 100 2.25

123 CVF YF and HSC 30.556 100 2

124 CVF YF and CCF 25 100 2

125 CVF YF and SBF 30.556 100 2

126 PBS HSS and CKF 11.111 100 1.8

127 CBF HSS and CKF 11.111 100 2

128 TF HSS and CKF 11.111 100 1.895

129 BQF HSS and CKF 11.111 100 2.25

130 CCF HSS and CKF 11.111 100 1.895

131 SBF HSS and CKF 11.111 100 1.714

132 PBS HSS and KF 22.222 100 1.8

133 PBS HSS and CBF 44.444 100 1.8

134 PBS HSS and TF 36.111 100 1.8

135 PBS HSS and BQF 16.667 100 1.8

136 PBS HSS and CBQ 16.667 100 1.8

137 PBS HSS and HSC 22.222 100 1.8

138 PBS HSS and CCF 33.333 100 1.8

139 PBS HSS and CVF 22.222 100 1.8

140 CBF HSS and KF 22.222 100 2

141 CCF HSS and KF 22.222 100 1.895

165

142 CBF HSS and BQF 16.667 100 2

143 CBF HSS and CBQ 16.667 100 2

144 CBF HSS and HSC 22.222 100 2

145 CBF HSS and CCF 33.333 100 2

146 CBF HSS and CVF 22.222 100 2

147 CCF HSS and BQF 16.667 100 1.895

148 SBF HSS and BQF 16.667 100 1.714

149 CCF HSS and CVF 22.222 100 1.895

150 CBF CKF and PBS 13.889 100 2

151 TF CKF and PBS 13.889 100 1.895

152 BQF CKF and PBS 13.889 100 2.25

153 CCF CKF and PBS 13.889 100 1.895

154 SBF CKF and PBS 13.889 100 1.714

155 BQF CKF and KF 27.778 100 2.25

156 CKF KF and BQF 27.778 100 2.571

157 CBQ CKF and KF 27.778 100 2.25

158 CVF CKF and KF 27.778 100 2

159 SBF CKF and KF 27.778 100 1.714

160 TF CKF and CBF 16.667 100 1.895

161 BQF CKF and CBF 16.667 100 2.25

162 CCF CKF and CBF 16.667 100 1.895

163 SBF CKF and CBF 16.667 100 1.714

164 BQF CKF and TF 22.222 100 2.25

165 SBF CKF and TF 22.222 100 1.714

166 BQF CKF and CBQ 36.111 100 2.25

167 BQF CKF and HSC 33.333 100 2.25

168 BQF CKF and CCF 30.556 100 2.25

169 BQF CKF and CVF 36.111 100 2.25

170 BQF CKF and SBF 36.111 100 2.25

171 CBQ CKF and HSC 33.333 100 2.25

172 CVF CKF and CBQ 36.111 100 2

173 CBQ CKF and CVF 36.111 100 2.25

174 CVF CKF and HSC 33.333 100 2

175 CBF PBS and KF 25 100 2

176 CCF PBS and KF 25 100 1.895

177 CBF PBS and BQF 19.444 100 2

178 CBF PBS and CBQ 19.444 100 2

179 CBF PBS and HSC 25 100 2

180 CBF PBS and CCF 36.111 100 2

181 CBF PBS and CVF 25 100 2

182 CBF PBS and SBF 33.333 100 2

166

183 CCF PBS and BQF 19.444 100 1.895

184 SBF PBS and BQF 19.444 100 1.714

185 CCF PBS and CVF 25 100 1.895

186 CCF KF and CBF 27.778 100 1.895

187 CBQ KF and BQF 27.778 100 2.25

188 CVF KF and BQF 27.778 100 2

189 SBF KF and BQF 27.778 100 1.714

190 CVF KF and CBQ 30.556 100 2

191 SBF KF and CBQ 30.556 100 1.714

192 CBF TF and CCF 30.556 100 2

193 CCF CBF and BQF 22.222 100 1.895

194 SBF CBF and BQF 22.222 100 1.714

195 CCF CBF and CVF 27.778 100 1.895

196 SBF TF and BQF 25 100 1.714

197 CVF BQF and CBQ 38.889 100 2

198 CBQ BQF and CVF 38.889 100 2.25

199 CVF CBQ and HSC 38.889 100 2

200 CBQ HSC and CVF 38.889 100 2.25

201 CVF CBQ and CCF 33.333 100 2

202 CVF CBQ and SBF 38.889 100 2

203 PBS CBF 50 94.444 1.7

204 CBF HSS and PBS 47.222 94.118 1.882

205 HSS PBS and CBF 47.222 94.118 1.783

206 SBF BQF 44.444 93.75 1.607

207 CVF CBQ 44.444 93.75 1.875

208 BQF CBQ and CVF 41.667 93.333 2.1

209 HSC CBQ and CVF 41.667 93.333 1.867

210 SBF CBQ and CVF 41.667 93.333 1.6

211 CBQ CKF 38.889 92.857 2.089

212 CVF CKF 38.889 92.857 1.857

213 SBF CKF 38.889 92.857 1.592

214 CBQ CKF and BQF 38.889 92.857 2.089

215 CKF BQF and CBQ 38.889 92.857 2.388

216 CVF CKF and BQF 38.889 92.857 1.857

217 CKF BQF and CVF 38.889 92.857 2.388

218 SBF CKF and BQF 38.889 92.857 1.592

219 PBS CBF and TF 38.889 92.857 1.671

220 PBS CBF and CCF 38.889 92.857 1.671

221 HSC BQF and CBQ 38.889 92.857 1.857

222 CBQ BQF and HSC 38.889 92.857 2.089

223 BQF CBQ and HSC 38.889 92.857 2.089

167

224 SBF BQF and CBQ 38.889 92.857 1.592

225 BQF CBQ and SBF 38.889 92.857 2.089

226 CVF BQF and HSC 38.889 92.857 1.857

227 HSC BQF and CVF 38.889 92.857 1.857

228 BQF HSC and CVF 38.889 92.857 2.089

229 SBF BQF and HSC 38.889 92.857 1.592

230 SBF BQF and CVF 38.889 92.857 1.592

231 SBF CBQ and HSC 38.889 92.857 1.592

232 HSC CBQ and SBF 38.889 92.857 1.857

233 SBF HSC and CVF 38.889 92.857 1.592

234 YF CKF and CBQ 36.111 92.308 2.769

235 YF CKF and CVF 36.111 92.308 2.769

236 HSS PBS and CCF 36.111 92.308 1.749

237 CBF HSS and TF 36.111 92.308 1.846

238 HSC CKF and CBQ 36.111 92.308 1.846

239 SBF CKF and CBQ 36.111 92.308 1.582

240 CBQ CKF and SBF 36.111 92.308 2.077

241 HSC CKF and CVF 36.111 92.308 1.846

242 SBF CKF and CVF 36.111 92.308 1.582

243 CVF CKF and SBF 36.111 92.308 1.846

244 PBS CBF and SBF 36.111 92.308 1.662

245 SBF KF and CVF 36.111 92.308 1.582

246 CVF KF and SBF 36.111 92.308 1.846

247 CCF CBF and SBF 36.111 92.308 1.749

248 SBF BQF and CCF 36.111 92.308 1.582

249 YF PSF 33.333 91.667 2.75

250 PSF YF 33.333 91.667 2.75

251 CKF PSF 33.333 91.667 2.357

252 BQF PSF 33.333 91.667 2.062

253 CBQ PSF 33.333 91.667 2.062

254 HSC PSF 33.333 91.667 1.833

255 CVF PSF 33.333 91.667 1.833

256 HSC YF 33.333 91.667 1.833

257 SBF YF 33.333 91.667 1.571

258 PSF YF and CKF 33.333 91.667 2.75

259 PSF YF and BQF 33.333 91.667 2.75

260 PSF YF and CBQ 33.333 91.667 2.75

261 PSF YF and CVF 33.333 91.667 2.75

262 PSF CKF and HSC 33.333 91.667 2.75

263 HSC YF and CKF 33.333 91.667 1.833

264 YF CKF and HSC 33.333 91.667 2.75

168

265 SBF YF and CKF 33.333 91.667 1.571

266 HSC YF and BQF 33.333 91.667 1.833

267 SBF YF and BQF 33.333 91.667 1.571

268 HSC YF and CBQ 33.333 91.667 1.833

269 SBF YF and CBQ 33.333 91.667 1.571

270 HSC YF and CVF 33.333 91.667 1.833

271 SBF YF and CVF 33.333 91.667 1.571

272 PBS HSS and SBF 33.333 91.667 1.65

273 HSS PBS and SBF 33.333 91.667 1.737

274 CBF HSS and SBF 33.333 91.667 1.833

275 SBF CKF and HSC 33.333 91.667 1.571

276 CCF PBS and SBF 33.333 91.667 1.737

277 SBF KF and HSC 33.333 91.667 1.571

278 BQF CBQ and CCF 33.333 91.667 2.062

279 HSC CBQ and CCF 33.333 91.667 1.833

280 SBF CBQ and CCF 33.333 91.667 1.571

281 SBF PSF and YF 30.556 90.909 1.558

282 PSF YF and SBF 30.556 90.909 2.727

283 SBF PSF and CKF 30.556 90.909 1.558

284 SBF PSF and BQF 30.556 90.909 1.558

285 SBF PSF and CBQ 30.556 90.909 1.558

286 SBF PSF and HSC 30.556 90.909 1.558

287 SBF PSF and CVF 30.556 90.909 1.558

288 SBF YF and HSC 30.556 90.909 1.558

289 HSC YF and SBF 30.556 90.909 1.818

290 CKF KF and CBQ 30.556 90.909 2.338

291 CBQ CKF and CCF 30.556 90.909 2.045

292 CVF CKF and CCF 30.556 90.909 1.818

293 SBF CKF and CCF 30.556 90.909 1.558

294 PBS TF and CCF 30.556 90.909 1.636

295 BQF KF and CBQ 30.556 90.909 2.045

296 HSC KF and CBQ 30.556 90.909 1.818

297 SBF TF and CVF 30.556 90.909 1.558

298 CVF TF and SBF 30.556 90.909 1.818

299 YF CKF and KF 27.778 90 2.7

300 YF KF and BQF 27.778 90 2.7

301 HSC CKF and KF 27.778 90 1.8

302 PBS KF and CBF 27.778 90 1.62

303 PBS CBF and HSC 27.778 90 1.62

304 PBS CBF and CVF 27.778 90 1.62

305 CVF KF and TF 27.778 90 1.8

169

306 HSC KF and BQF 27.778 90 1.8

307 TF CBF and CVF 27.778 90 1.705

308 CCF CBF and HSC 27.778 90 1.705

309 SBF CBF and CVF 27.778 90 1.543 Table 17 Scoring for All Rule Sets (South West China Region)



ID Probability Rule





12 1 if Yolk = 1 then 1.000










11 1 if Kimchi flavor = 1 and Cumin BBQ = 1 then 1.000





10 1 if Kimchi flavor = 1 and BBQ flavor = 1 then 1.000












16 0.938 if Cumin BBQ = 1 then 1.000

170





13 0.923 if Kimchi flavor = 1 and Sauced beef flavor = 1 then 1.000




10 0.9 if Kimchi flavor = 1 and Tomato flavor = 1 then 1.000



ID Probability Rule











12 1 if Yolk = 1 then 1.000













14 0.929 if BBQ flavor = 1 and Cumin BBQ = 1 then 1.000

14 0.929 if BBQ flavor = 1 and Chives flavor = 1 then 1.000


11 0.909 if Kimchi flavor = 1 and Cumin BBQ = 1 then 1.000



171

ID Probability Rule






ID Probability Rule





10 0.9 if Curry beef = 1 and Chives flavor = 1 then 1.000



ID Probability Rule












11 1 if Kimchi flavor = 1 and Cumin BBQ = 1 then 1.000















172


13 0.923 if Kimchi flavor = 1 and Chives flavor = 1 then 1.000


12 0.917 if Yolk = 1 then 1.000






12 0.917 if Kimchi flavor = 1 and Hot sichuan = 1 then 1.000






11 0.909 if Tomato flavor = 1 and Chives flavor = 1 then 1.000








ID Probability Rule



12 0.917 if Yolk = 1 then 1.000








ID Probability Rule




16 1 if spices salt = 1 and Curry beef = 1 then 1.000



173




18 0.944 if Curry beef = 1 then 1.000





11 0.909 if Tomato flavor = 1 and Cheese corn flavor = 1 then 1.000

10 0.9 if Kimchi flavor = 1 and Curry beef = 1 then 1.000

10 0.9 if Curry beef = 1 and Hot sichuan = 1 then 1.000



1.000 = Yolk Flavor

ID Probability Rule


















10 0.9 if Kimchi flavor = 1 and BBQ flavor = 1 then 1.000

BBQ Flavor Rule Set

1.000 = BBQ Flavor

ID Probability Rule






174






12 1 if Yolk = 1 then 1.000






























Curry Flavor Rule Set

1.000 = Curry Flavor

ID Probability Rule




175



9 1 if Pepper beef steak = 1 and Hot sichuan = 1 then 1.000

13 1 if Pepper beef steak = 1 and Cheese corn flavor = 1 then 1.000










17 0.941 if spices salt = 1 and Pepper beef steak = 1 then 1.000





ID Probability Rule











12 1 if Yolk = 1 then 1.000











14 1 if BBQ flavor = 1 and Chives flavor = 1 then 1.000

176

14 1 if Hot sichuan = 1 and Chives flavor = 1 then 1.000















ID Probability Rule



17 0.941 if Pepper beef steak = 1 and Curry beef = 1 then 1.000



ID Probability Rule




















177



12 0.917 if Yolk = 1 then 1.000





10 0.9 if Kimchi flavor = 1 and BBQ flavor = 1 then 1.000



ID Probability Rule











10 1 if Kimchi flavor = 1 and Curry beef = 1 then 1.000





10 0.9 if Curry beef = 1 and Hot sichuan = 1 then 1.000 Table 8 Scoring information for Top 10 Rules in South West China Region Year 2013 2014 2015 2014 2015 2013 2015 2013 2014 2013

Month Jan May Sep Feb Jun Nov Mar Aug Dec May Tomato flavor 1 1 1 0 0 1 1 1 0 0

Crab flavor 1 0 0 0 0 1 1 0 1 0 Sauced beef 0 0 1 0 1 1 1 1 1 0

Cheese corn 0 0 1 0 0 1 0 1 1 0

BBQ flavor 0 0 1 0 1 0 1 1 0 0 Pepper beef steak 1 1 1 0 0 1 0 1 1 0

Chicken flavor 0 0 1 0 1 0 1 1 0 0 Curry beef 1 0 1 0 0 1 0 1 1 0

Cumin BBQ 0 0 1 0 1 0 1 0 1 0

spices salt 1 0 1 0 0 1 0 1 1 0

178

Pork steak 0 0 1 0 1 0 1 0 0 0 Yolk 0 0 1 0 1 0 1 0 0 0

Kimchi flavor 0 0 1 0 1 1 1 0 1 0 Chives flavor 0 0 1 0 1 1 1 0 1 0

Hot sichuan 0 0 1 0 1 0 1 0 1 0

Scoring Information Prediction 1 CBQ BQF

Confidence 1 0.92

9 0.93

3

Rule ID 1 212 209 Prediction 2 CVF CF

Confidence 2 0.92

9 0.90

9

Rule ID 2 213 293 Prediction 3 TF

Confidence 3 0.9 Rule ID 3 305

Documents

thesis Yuhan Wang (1)