JDCTA - IMISSU Single Sign On of Udayana University...analyzes the transaction data of companies engaged in the distribution of pharmaceutical ingredients. Data transaction of the

JDCTA

Volume 10, Num

ber 4, Sep. 30, 2016

International Journal of Digital Content Technology and its Applications (JDCTA)

Volume 10, Number 4, Sep. 30, 2016 ISSN : 1975-9339(Print), 2233-9310(Online)A Publication of the Advanced Institute of Convergence Information Technology(AICIT)

90, Centum jungang-ro, Haeundae-gu, Busan, 612-050, Rep.of Korea | Website : http://www.aicit.org/jdcta | E-mail : [email protected]

International Journalof Digital Content Technology and

its Applications

International Journal of Digital Content Technology and its Applications

Volume 10, Number 4, Sep. 30, 2016 ISSN : 1975-9339(Print), 2233-9310(Online)

JDCTAJDCTA

International Journal of Digital Content Technology and its Applications

Contents:

Relation between knowledge, attitude, and performance ability of infant

CPR of pediatric ward nurses .............................................................................................................. 1

In-Sook Park, Jae-Woo Oh

Technical feasibility analysis to integrate pilot test of desk as a Service on

Computational Laboratory in Academic Environments ............................................. 9

Carlos Enrique Montenegro Marin, Paulo Alonso Gaona-Garcia, Joaquin Fernando Sánchez

Cifuentes

Customer Segmentation Using Particle Swarm Optimization and K-Means

Algorithm .............................................................................................................................................................. 22

Dewa Ayu Agung Yunita Primandari, Ketut Gede Darma Putra, Made Sukarsa

Customer Segmentation Using Particle Swarm Optimization

and K-Means Algorithm

1I Dewa Ayu Agung Yunita Primandari, 2I Ketut Gede Darma Putra, 3I Made Sukarsa 1,2,3

Department of Information Technology Engineering Faculty Udayana University,

Jimbaran, Badung, Bali, Indonesia, [email protected], [email protected],

[email protected]

Abstract Customer segmentation is an implementation of clustering in the data mining process. Customer

segmentation divides customers into certain classes to help a company to understand each customer.

This paper analyzes 33,441 rows of a transaction data and transforms it into 914 rows of Recency,

Frequency, and Monetary data (RFM) to identify potential customer. Clustering method uses are the

combination of Particle Swarm Optimization (PSO) and K-Means algorithm. The combination of these

algorithms aims to take advantages of both algorithms and remove their weakness. K-means is very

sensitive to initialize the cluster center because it does randomly. PSO is uses to optimize the cluster

center and help K-Means to cluster better. The clustering experiment uses several numbers of a cluster.

The best numbering of the cluster for this experiments are two clusters according to Davies-bouldin

Index (DBI) method.

Keywords: Data Mining, Customer Segmentation, RFM Model, K-Means, Particle Swarm

Optimization, Davies-Bouldin Index

1. Introduction

Nowadays, the main goal of a company is to strengthen customer relationships, so that the company

can get a competitive advantage on the market and make a bigger profit. This means that companies

should develop their skills in terms of identification customer expectations and then provide customers

with more benefits and satisfaction resulting therefrom [1]. Customer Relationship Management (CRM)

can help a company to manage their relationship with customers. CRM is a strategy to build, manage,

and strengthen loyal and long-lasting customer [2]. Many companies often use data mining techniques

and CRM’s concepts, which helps to identify customers by using customer segmentation [3].

Customer segmentation is the process of dividing customers into different groups, with members of

each group having similar needs, characteristics, or behaviors [4]. Customer segmentation analyzes a

company’s data transaction to find a profitable customer. First, the company’s data need to change into

Recency, Frequency, and Monetary (RFM) value. RFM is a technique used for analyzing customer

behavior such as how recently a customer has purchased (Recency), how often the customer purchase

(Frequency), and how much the customer spends (Monetary) [5]. The attribute of RFM model can

describe by linguistic variables. As an example, the linguistic variable of Recency is described by the

terms ‘long ago’ and ‘very recent’, the attribute of Frequency by terms ‘rare’ and ‘frequent’ and the third

attribute Monetary is divided into the terms ‘low value’ and ‘high value’ [6].

K-Means with RFM model is the clustering method used in this research. In similar studies revealed

that K-Means with RFM model has the ability to process huge customers’ data [7], but the selection of

the central point of K-Means that done randomly, made the clustering process become slowly. The study

was then refined by using a combination of K-Means and PSO Algorithm [8]. In that study concluded

that the lack of K-Means in determining the center point of the cluster can be overcome by combining

the K-Means and PSO resulting in a classification that is more accurate and efficient. This research

analyzes the transaction data of companies engaged in the distribution of pharmaceutical ingredients.

Data transaction of the company segmented using a combination of K-Means and PSO methods with

RFM model, to get the most potential customers for the company.

Customer Segmentation Using Particle Swarm Optimization and K-Means Algorithm Dewa Ayu Agung Yunita Primandari, Ketut Gede Darma Putra, Made Sukarsa

International Journal of Digital Content Technology and its Applications(JDCTA) Volume10, Number4, Sep. 2016

22

2. Methodology

The input of this customer segmentation’s system is monthly transaction data from May 2014 until

July 2015. It consists of 33,741 rows of data. It stored into transaction table which has consist of

no_invoice, transaction_date, sales_id, customer_id and total_transaction field.

Figure 1. Data Transformation Scheme

Figure 1 explaining the data transformation scheme. Transaction table simplified into a new table that

called standard table which contains transaction_date, customer_id, and total_transcation field.

transaction_date can represent recency attribute in the declared value of the last transaction done.

customer_id is the field that can be used to calculate frequency value based on its appearance.

total_transcation can be used to calculate the monetary value based customer_id field. The data contained

in standard table calculated into RFM value then stored in the table called rfm table. Standard table

transformed into 914 rows of RFM.

2.1 Data normalization

The process of data normalization is performed after the data transformation. Data normalization is

the process of scaling value data attributes that can fall into certain range [9]. Clustering process may

consist of a various interval of values that lead to differences in distance between one values with others,

the need for data to process the normalization has an average value of 0 or zero mean. A data or features

can be normalized by the following min-max normalization method.

𝑥′ =𝑥−𝑚𝑖𝑛𝐴

𝑚𝑎𝑥𝐴−𝑚𝑖𝑛𝐴(𝑛𝑒𝑤_𝑚𝑎𝑥𝐴 − 𝑛𝑒𝑤_𝑚𝑖𝑛𝐴) + 𝑛𝑒𝑤_𝑚𝑖𝑛𝐴 (1)

minA and maxA are the minimum and maximum values of attributes.

2.2 Clustering

This paper used a combination algorithm based on PSO and K-Means to clustered data. The purpose

of this algorithm is to take advantages of both algorithms and remove their weakness. When comes to

clustering K-means is very sensitive to initializing the cluster center because it is done randomly. K-

Means would work well in optimal cluster center. PSO is one of the swarm intelligence methods that can

use to optimize the cluster center [10]. PSO was presented according to animal social interactions. Every

particle tries to move toward a final solution by adjusting its path and moving toward the best personal

experience and also the best swarm experience [11]. Each particle updates its location with the following

two equations.

𝑉𝑖′ = 𝜔𝑉𝑖 + 𝑐1𝑟𝑎𝑛𝑑1(𝑃𝑏𝑒𝑠𝑡 − 𝑋𝑖) + 𝑐2𝑟𝑎𝑛𝑑2(𝑃𝑔𝑏𝑒𝑠𝑡 − 𝑋𝑖)` (2)

𝑋𝑖′ = 𝑋𝑖 + 𝑉,

′ (3)



23

Rand1and rand2 is random numbers between 0 and 1, c1 and c2 are the cognitive and social learning

rates, Pbest are the best location of particle, Pgbest is the global best location value, ω is the inertia, i is the

dimension of the data and t is the number of iteration.

In this research K-Means inheriting cluster center from PSO result and assigned each data to the

closest cluster center using Euclidean Distance Equation [7].

𝑑𝑒𝑢𝑐 = ∑ √(𝑧𝑖 − 𝑋𝑖)2𝑛

𝑖=0 (4)

Recalculate the values of the centroids. The value of the centroid’s field is updated, taken as the

average of the values of the entities attributes that are part of the cluster. Repeat iteratively until the

convergence is achieved. The cluster that has been formed then being validated using DBI Method. DBI

is based on similarity measure of cluster (Rij) whose bases are the dispersion measure of the cluster (si)

and the cluster dissimilarity measure (dij) [12].

2.3 Data modeling

Cluster formed going through the data modeling process. This process compared the average value

of each cluster and compared with the range of values in the domain value of RFM to find the grade for

each cluster. Each variable of R, F and M has three linguistic variables and domain value. Linguistic

variable and domain value for each average are shown in Table 1

Table 1. Linguistic Variable and Domain Value for Each of Average R, F, M

Attributes Linguistic Variable Domain Value

Recency Long Time Ago

A Little Longer

Recently

0 ≤ r ≤ 200 days

201 days<r <501 days

501 days < r

Frequency Seldom

Rather Frequently

Often

0 ≤ f < 50 transactions

51 transactions < f < 150 transactions

150 transactions < f

Monetary Low

Medium

High

0 ≤ m < 1,000,000 rupiahs

1,000,001 rupiahs < m < 5,000,000 rupiahs

5,000,001 rupiahs < m

Data modeling process is done by calculating the average RFM of each cluster to all classes of RFM

models include in Table 2 using equation Zumstein [6].

𝜇𝐴(𝑥) = (∏ 𝜇𝑖𝑚𝑖=1 (𝑥))1−𝑦(1 − ∏ (1 − (𝜇𝑖

𝑚𝑖=1 (𝑥)))𝑦 (5)

Each class in the Model RFM has a customer label that states the characteristics of each class of

customers. Class description for each cluster can be seen in Table 2

Table 2. Description Linguistic Variables and Labels Costumers

Description of Linguistic Variables Class

Recency Frequency Monetary

Recently Seldom Low Dormant D

Recently Seldom Medium Dormant A

Recently Seldom High Occasional A

Recently Quite Often Low Everyday D

Recently Quite Often Medium Golden D

Recently Quite Often High Superstar D

Recently Often Low Everyday A

Recently Often Medium Golden A

Recently Often High Superstar A

Rather Long-Time Seldom Low Dormant E

Rather Long-Time Seldom Medium Dormant B

Rather Long-Time Seldom High Occasional B

Rather Long-Time Quite Often Low Everyday E



24

Description of Linguistic Variables Class

Recency Frequency Monetary Rather Long-Time Quite Often Medium Golden E

Rather Long-Time Quite Often High Superstar E

Rather Long-Time Often Low Everyday B

Rather Long-Time Often Medium Golden B

Rather Long-Time Often High Superstar B

Long-Time Seldom Low Dormant F

Long-Time Seldom Medium Dormant C

Long-Time Seldom High Occasional C

Long-Time Quite Often Low Everyday F

Long-Time Quite Often Medium Golden F

Long-Time Quite Often High Superstar F

Long-Time Often Low Everyday C

Long-Time Often Medium Golden C

Long-Time Often High Superstar C

There are five classes of customers. The most loyal customer with the highest value called Superstar

Customers. The second highest value called Golden Customers. Occasional Customers are customers

whose have the second lowest value. Everyday are customers with increasing number of visits but low

transaction value and Dormant Customers are customers whose have the lowest value. Each class

consists of three until five subclasses. For example, Golden customers consist of golden A, golden B,

golden C, golden D and Golden E.

3. Experiment and result

The experiment began with a selection of data from enterprise transaction data. Transaction data was

converted into a form RFM. Transaction data was converted into a form of RFM that had been

normalized using the min and max normalization method. The value of RFM was simplified into a range

of values from 1 to 0. The data that had been normalized will further be segmented. The segmentation

used two methods: the method of K-Means and K-Means methods combined with PSO. The experiments

used two until four clusters and 10 iterations produced optimal center points through the method of PSO.

(a) 2 cluster

(b) 4 cluster

Figure 2. Center Points Optimization Using PSO



25

PSO obtained an optimal center point which was used to perform clustering on the K-Means method.

Table 3 is an optimal center point obtained.

Table 3. The Value of Optimized Cluster Center Using 2 and 4 Clusters

Number of Cluster Recency Frequency Monetary

2 cluster -0.0919 -9.5080e-05 -2.3345e-05

-0.0352 -4.14892-04 -1.7413e-04

4 cluster 0.2442 -0.0015 -0.0014

0.0783 -1.0015e-04 -2.5278e-05

-0.0139 -3.4009e-04 -1.3324e-04

-0.0105 -1.1684e-04 -2.9462e-05

Figure 3 shows the segmentation result using K-Means and PSO + K-Means. The segmentation with

K-Means method used a random cluster center and the segmentation with PSO + K-Means Method used

the optimal cluster center from PSO Method process.

(a) 2 cluster

(b) 4 cluster

Figure 3. Comparison clustering process between K-Means and PSO+K-Means

The segmentation result using K-Means and PSO + K-Means was shown in Table 4.



26

Table 4. Segmentation Result Using 4 Cluster

Method Cluster Recency Frequency Monetary Label

K-Means

using 2

clusters

2 444 (Rather Long-

Time)

27 (Seldom) 8171852 (Medium) Dormant B

1 688 (Recently) 174 (Often) 30573045 (High) Superstar A

PSO+K-

Means

using 2

clusters


2 444 (Rather Long-

Time)

27 (Seldom) 8171852 (Medium) Dormant B

K-Means

using 4

clusters

3 697 (Quite Often) 106 (Quite Often) 10371744 (High) Superstar D

1 381 (Rather Long-

Time)

25 (Seldom) 10900387 (High) Occasional B

4 718 (Recently 468 (Often) 105686107 (High) Superstar A

2 590 (Recently) 42 (Seldom) 5995413 (Medium) Dormant A

PSO+K-

Means

using 4

clusters

3 580 (Recently) 40 (Seldom) 6749484 (Medium) Dormant A

1 698 (Recently) 114 (Quite Often) 10900040 (High) Superstar D


2 364 (Rather Long-

Time)

27 (Seldom) 10266023 (High) Occasional B

The experiments above show that the results of the segmentation between K-Means Method and PSO

+ K-Means Method didn't have significant differences judged by the labels of customer that was

produced. It suggests that the optimization of the center point of the cluster using PSO didn't have an

impact on the outcome of the K-Means clustering. Figure 4 show cluster validation process using DBI

shows that the optimum clusters needed for both algorithms are two. The second cluster has 0.5 DBI’s

index either with K-Means Method or with PSO+K-Means Method. The optimum cluster according to

DBI Index is cluster with the lowest DBI Index.

Figure 4. Cluster Validation of K-Means and PSO+K-Means Result

PSO capability in optimizing the K-Means was described through a comparison table of the speed of

execution time K-Means clustering using PSO and without using PSO. This experiment used the same

amount of data and the same iteration.

Table 5. Comparison of K-Means and PSO + K Means

Number of Cluster Execution time of K-Means Execution time of K-Means PSO+K-

Means

2 1.317107 s 0.098473 s

4 1.472539 s 0.128821 s



27

Table 5 explains that the execution time of PSO + K-Means is faster than execution using K-Means

method. The effectiveness of the combination method of PSO and K-Means can be proven from the

execution time which was faster than K-Means method. Optimization of the center point by PSO was

proven to make the process faster than the clustering process using K-Means clustering because the

central point was no longer being selected randomly.

4. Conclusion

Segmentation results with both K-Means and PSO + K-Means can perform segmentation well, seen

from various customer classes’ result. The customer classes that has been produced were Dormant B,

Superstar A, Superstar D, Occasional B, Dormant A, and Occasional B. The result of the segmentation

between K-Means and PSO + K-Means did not produce a significant difference, but judged from the

execution time indicated that the PSO + K-Means combined method can perform faster than the

segmentation using K-Means. With 2 clusters, PSO + K-Means method could perform clustering

1.218634 seconds faster than used K-Means Method only and 1.343718 faster with 4 clusters.

5. Acknowledgment

Our thanks go to Department of Information Technology Udayana University, Bali, Indonesia who

has helped organize this research.

6. References

[1] Dorota Jelonek, “The Evolution of Customer Relationship Management System”, Recent

Advances in Computer Science, p. 29-33. 2015.

[2] Konstantinos Tsiptsis, Antonios Charianopoulos, “Data Mining Techniques in CRM: Inside

Customer Segmentation”, John Wiley and Sons, ltd., England, 2009.

[3] Hasan Ziafat, Majid Shakeri, “Using Data Mining Techniques in Customer Segmentation”,

Journal of Engineering Research and Applications, Vol. 4, pp. 70-79, 2014.

[4] Vahid Golmah, “A Case Study of Applying SOM in Market Segmentation of Automobile

Insurance Customers”, International Journal of Database Theory and Application, Vol. 7, No. 1,

pp. 25-36, 2014.

[5] Derya Birant, “Data Mining Using RFM Analysis”, InTech, Croatia, 2011.

[6] Darius Zumstein, “Customer Performance Measurement: Analysis of the Benefit of a Fuzzy

Classification Approach in Customer Relationship Management”, (Thesis), University of

Fribourg, 2007.

[7] Hasitha Indika Arumawadu. R. M Kapila Tharanga Rathnayaka, S.K. Illangarathne, “Mining

Profability of Telecommunication Customers Using K-Means Clustering”, Journal of Data

Analysis and Information Processing, Scientific Research Publishing, Vol. 3, pp. 63-71, 2015.

[8] C.Y. Chiu, Y.F. Chen, I.T. Kuo, He Chun Ku, “An Intelligent Market Segmentation System Using

K-Means and Particle Swarm Optimization”, Journal Expert System with Applications: An

International Journal, Pergamon Press Inc, Vol. 36, pp. 4558-4565, 2009.

[9] Jiawei Han, Jian Pei, Micheline Kamber. “Data Mining, Southeast Asia Edition”. Morgan

Kaufmann Publishers, USA, 2006.

[10] Junyan Chen, “Hybrid Clustering Algorithm Based on PSO with the Multidimensional

Asynchronism and Stochastic Disturbance Method”. Journal of Theoretical and Applied

Information Technology, Vol. 46 No. 1, pp. 434-440.

[11] Satyobroto Talukder, “Mathematical Modelling and Application of Particle Swarm

Optimization”, (Master Thesis), Blekinge Institute of Technology, Sweden, 2011.

[12] D. L. Davies, D. W. Bouldin, “Cluster Separation Measure”, IEEE Transactions on Pattern

Analysis and Machine Intelligence, Vol. 1, No. 2, pp. 95-104, 1979.



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JDCTA - IMISSU Single Sign On of Udayana University...analyzes the transaction data of companies engaged in the distribution of pharmaceutical ingredients. Data transaction of the