International Journal of Research ISSN NO:2236-6124ijrpublisher.com/gallery/208-may-1247.pdf · market segmentation and store layout; and manufacturers, for quality control and

AUTOMATIC MINING FACETS FOR QUERIES FROM THEIR SEARCH RESULTS

USING DATA MINING ALGORITHM QUERYMINER

1 B. Suryanarayana Murthy, 2 Dr. V. Purnachandra Rao

1Research Scholar, Department of Computer Science, Dravidian University, Kuppam

2Professor of CSE/ IT, MLR Institute of Technology, Dundigal, Hyderabad.

Abstract:

Data mining has been used intensively and extensively by many organizations. In

healthcare, data mining is becoming increasingly popular, if not increasingly essential. Data

mining applications can greatly benefit all parties involved in the healthcare industry. data

mining can help healthcare insurers detect fraud and abuse, healthcare organizations make

customer relationship management decisions, physicians identify effective treatments and best

practices, and patients receive better and more affordable healthcare services. The huge amounts

of data generated by healthcare transactions are too complex and voluminous to be processed and

analyzed by traditional methods. Data mining provides the methodology and technology to

transform these mounds of data into useful information for decision making. This manuscript

was proposing the algorithm for the healthcare system which is called query facets algorithm,

which can fetches data from the server based on query.

Keywords: Query Facet, Query Miner Algorithm, Healthcare system, headache, Data

Attributes, Simulation.

1 Introduction:

Data mining can be defined as the

process of finding previously unknown

patterns and trends in databases and using

that information to build predictive models.1

Alternatively, it can be defined as the

process of data selection and exploration

and building models using vast data stores

to uncover previously unknown patterns.[1]

Data mining is not new it has been used

intensively and extensively by financial

institutions, for credit scoring and fraud

detection; marketers, for direct marketing

and cross-selling or up-selling; retailers, for

market segmentation and store layout; and

manufacturers, for quality control and

maintenance scheduling. In healthcare, data

mining is becoming increasingly popular, if

not increasingly essential. Several factors

have motivated the use of data mining

applications in healthcare. The existence of

medical insurance fraud and abuse, for

example, has led many healthcare insurers to

attempt to [2] reduce their losses by using

data mining tools to help them find and

track offenders.[3] Fraud detection using

data mining applications is prevalent in the

commercial world, for example, in the

detection of fraudulent credit card

transactions. Recently, there have been

reports of successful data mining

applications in healthcare fraud and abuse

detection Another factor is that the huge

amounts of data generated by healthcare

transactions are too complex and

voluminous to be processed and analyzed by

traditional methods. Data mining can

improve decision-making by discovering

International Journal of Research

Volume VIII, Issue V, MAY/2019

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patterns and trends in large amounts of

complex data.4 Such analysis has become

increasingly essential as financial pressures

have heightened the need for healthcare

organizations to make decisions based on

the analysis of clinical and financial data.

Insights gained from data mining can

influence cost, revenue, and operating

efficiency while maintaining a high level of

care.[4] Healthcare organizations that

perform data mining are better positioned to

meet their long-term needs

Yet another factor motivating the use of

data mining applications in healthcare is

the realization that data mining can

generate information that is very useful to

all parties involved in the healthcare

industry. For example, data mining

applications can help healthcare insurers

detect fraud and abuse, and healthcare

providers can gain assistance in making

decisions, for example, in customer

relationship management. Data mining

applications also can benefit healthcare

providers, such as hospitals, clinics and

physicians, and patients, for example, by

identifying effective treatments and best

practices There are also other factors

boosting data mining’s popularity. For

instance, as a result of the Balanced Budget

Act of 1997, the Centers for Medicare and

Medicaid Services must implement a

prospective payment system based on

classifying patients into case-mix groups,

using empirical evidence that resource use

within each case-mix group is relatively

constant. CMS has used data mining to

develop a prospective payment system for

inpatient rehabilitation.[5] The healthcare

industry can benefit greatly from data

mining applications. The objective of this

article is to explore relevant data mining

applications by first examining data

mining methodology and techniques; then,

classifying potential data mining

applications in healthcare; next giving an

illustration of a healthcare data mining

application; and finally, highlighting the

limitations of data mining and offering

some future directions.

1.1 Data Mining

History of Data Base and Data Mining

development and the history represented in

the Fig.1. The data mining system started

from the year of 1960s and earlier. In this,

the data mining is simply on file processing.

The next stage its Database management

Systems to be started year of 1970s early to

1980s. In this OLTP, Data modeling tools

and Query processing are worked. From

database management system there three

broad categories to be worked. First one is

Advanced Database Systems, this evaluated

year of Mid-1980s to present in this Data

models and Application oriented process are

worked.

Fig.1. History of Database Systems and

Data Mining

The Second part is Data Warehousing and

Data Mining worked since the year of the

late 1980s to present. The third part is Web

based Database Systems this started from

1990s to present and in this Web mining and



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XML based database systems are included.

These three broad categories are joined and

create the new process that’s called New

generation of the Integrated Information

system is started in 2000[6]

Data mining can be considered a relatively

recently developed methodology and

technology, coming into prominence only in

1994[7] It aims to identify valid, novel,

potentially useful, and understandable

correlations and patterns in data11 by

combing through copious data sets to sniff

out patterns that are too subtle or complex

for humans to detect.[8] Cross-Industry

Standard Process for Data Mining, or

CRISP-DM .Business understanding is

critical because it identifies the business

objectives and, thus, the success criteria of

data mining projects. Further, as the term

“data mining” implies, data is a crucial

component no data means no mining.

Hence, CRISP-DM includes data

understanding and data preparation in other

words, sampling and data transformation as

essential antecedents for modeling. The

modeling stage is the actual data analysis.

Most data mining software include online

analytical processing; traditional statistical

methods, such as cluster analysis,

discriminate analysis and regression

analysis; and non-traditional statistical

analysis, such as neural networks, decision

trees, link analysis and association analysis.

This extensive range of techniques is not

surprising in light of the fact that data

mining has been viewed as the offspring of

three different disciplines, namely database

management, statistics, and computer

science, including artificial intelligence and

machine learning. The evaluation stage

enables the comparison of models and

results from any data mining model by using

a common yardstick, such as lift charts,

profit charts, or diagnostic classification

charts. Data mining techniques can be

broadly classified based on what they can

do, namely description and visualization;

association and clustering; and classification

and estimation, which is predictive

modeling. Description and visualization can

contribute greatly towards understanding a

data set, especially a large one, and

detecting hidden patterns in data, especially

complicated data containing complex and

non-linear interactions. They are usually

performed before modeling is attempted and

represent data understanding in the CRISP-

DM methodology. In association, the

objective is to determine which variables go

together. For example, market-basket

analysis refers to a technique that generates

probabilistic statements such as, “If patients

undergo treatment A, there is a 0.35

probability that they will exhibit symptom

Z.” Such information can be useful for

investigating associative relationships in

healthcare. With clustering, the objective is

to group objects, such as patients, in such a

way that objects belonging to the same

cluster are similar and objects belonging to

different clusters are dissimilar. The most

common and important applications in data

mining probably involve predictive

modeling [9]. Classification refers to the

prediction of a target variable that is

categorical in nature, such as predicting

healthcare fraud vs. non fraud. Estimation,

on the other hand, refers to the prediction of

a target variable that is metric in nature,

such as predicting the length of stay or the

amount of resource utilization. For

predictive modeling, the data mining

techniques commonly used include

traditional statistics. They also include non-

traditional methods developed in the areas of

artificial intelligence and machine learning.

The two most important models of these are

neural networks and decision trees.

1.2 Techniques of Data Mining

There are many data mining techniques and

algorithms are available to discover



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meaningful pattern and rules [10].There are

many different techniques are as follow:

Classification: In classification, first

examine the features of newly presented

object and assign it to a predefined class for

example classify the credit applicants as

low, medium or high risk.[10]

Association: The main goal of association is

to establish the relationship between items

which exist in the market. The typical

examples of association modeling are

Market basket Analysis and cross selling

programs. The tools used for association

rule mining are apriori algorithm and weka

tool kit. [11]

Prediction: In this functionality, prediction

of some unknown or missing attributes

values based on other Information. For

example: Forecast the sale value for next

week based on available data.[12,13]

Clustering: In this, Data Mining organizes

data into meaningful sub-groups (clusters)

such that points within the group are similar

to each other, and as different as possible

from the points in the other groups. It is an

unsupervised classification. An effective

dynamic unsupervised clustering

algorithmic approach for market basket

analysis has been proposed by Verma et

al.[14].

Outlier Analysis: In this, Data Mining is

done to identify and explain exceptions. For

example, in case of Market Basket Data

Analysis, outlier can be some transaction

which happens unusually. [15]

2 Data Mining Application Areas

Data mining is driven in part by new

applications which require new capabilities

that are not currently being supplied by

today’s technology. These new applications

can be naturally into two broad categories

[16].

2.1 Applications:

Business and E-Commerce.

Scientific, Engineering and Health

Care Data

2.2 Data Mining Tasks:

Data mining tasks are mainly classified into

two broad categories they are, Predictive

model Descriptive model which was given

in the Fig .2.

Fig .2. Data mining models and tasks

2.3 Data Mining Applications In

Healthcare Sector

Healthcare industry today generates large

amounts of complex data about patients,

hospital resources, disease diagnosis,

electronic patient records, medical devices

etc. Larger amounts of data are a key

resource to be processed and analyzed for

knowledge extraction that enables support

for cost-savings and decision making. Data

mining applications in healthcare can be

grouped as the evaluation into broad

categories [17,18],

2.4 Treatment effectiveness



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Data mining applications can develop to

evaluate the effectiveness of medical

treatments. Data mining can deliver an

analysis of which course of action proves

effective by comparing and contrasting

causes, symptoms, and courses of

treatments.

2.5 Healthcare management

Data mining applications can be

developed to better identify and track

chronic disease states and high-risk patients,

design appropriate interventions, and reduce

the number of hospital admissions and

claims to aid healthcare management. Data

mining used to analyze massive volumes of

data and statistics to search for patterns that

might indicate an attack by bio-terrorists.

2.6 Customer relationship management

Customer relationship management is a

core approach to managing interactions

between commercial organizations-typically

banks and retailers-and their customers, it is

no less important in a healthcare context.

Customer interactions may occur through

call centers, physicians’ offices, billing

departments, inpatient settings, and

ambulatory care settings

2.7 Fraud and abuse

Detect fraud and abuses establish

norms and then identify unusual or abnormal

patterns of claims by physicians, clinics, or

others attempt in data mining applications.

Data mining applications fraud and abuse

applications can highlight inappropriate

prescriptions or referrals and fraudulent

insurance and medical claims.

2.8 Medical Device Industry

Healthcare system’s one important

point is medical device. For best

communication work this one is mostly

used. Mobile communications and low-cost

of wireless bio-sensors have paved the way

for development of mobile healthcare

applications that supply a convenient, safe

and constant way of monitoring of vital

signs of patients [19]. Ubiquitous Data

Stream Mining (UDM) techniques such as

light weight, one-pass data stream mining

algorithms can perform real-time analysis

on-board small/mobile devices while

considering available resources such as

battery charge and available memory.

2.9 Pharmaceutical Industry

The technology is being used to help

the pharmaceutical firms manage their

inventories and to develop new product and

services. A deep understanding of the

knowledge hidden in the Parma data is vital

to a firm’s competitive position and

organizational decision-making.

2.10 Hospital Management

Organizations including modern

hospitals are capable of generating and

collecting a huge amount of data.

Application of data mining to data stored in

a hospital information system in which

temporal behavior of global hospital

activities is visualized [20]. Three layers of

hospital management, Services for hospital

management, Services for medical staff,

Services for patients.

The following chart Fig. 3 , based on

Deloitte’s findings, shows how many

Smartphone users are aware of and/or

already using different AI-powered

applications on their devices, and it explains

that how many user have good awareness

about the data searching, which data is

useful for their regular usage in the daily

life.



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Fig. 3: Users awareness on data

searching in the daily life [21].

3 Previous work

Facet mining technique can be used for

different kinds of applications. This

technique is used for huge library database

applications and information science

research applications and to some computer

science research applications and

commercial search applications

Eg.Amazon.com need facets mining

application in order to get required data in

efficient manner in Web Queries data

mining.[22].

Different from query facet mining that

generates facets for each query without any

domain assumptions or prior knowledge,

some traditional faceted search approaches

are mainly built on a specific domain or

predefined facet categories. The problem of

automatically mining facet metadata and

mapping documents onto these categories

has been studied for years [23], A robust

review of faceted search is beyond the scope

of this paper. Dakka et al. [24, 25, 26]

developed methods to extract facet

hierarchies for a text corpus or a text

database then assign each document to those

facets. One main difference is that we aim to

mine several semantically coordinate lists of

items to guide users’ search, while Dakka’s

methods focus on building concept

hierarchies. E. Stoica et al. [27] proposed

Castanet to automatically generate domain-

specific faceted metadata from textual

descriptions of items based on existing

external lexical database Word Net.

FaSet model proposed by Bonino et al. [28]

focuses on using structured data in relational

database. It provides the formal definitions

of facet, facet space, focus, multi-

dimensional classification, etc. In addition

FaSet offers two search algorithms for

faceted navigation and search results

ranking respectively, which are

implemented in SQL.

Li et al. [29] presented a facet model with a

Directed Acyclic Graph (DAG) structure

based on the set theory. It is used in

Faceted-pedia system. This model gives the

formal definitions of category hierarchy,

facet, navigation path, faceted interface, and

provides the facet ranking operation based

on the navigation cost and pair wise

similarity among facets. facet model. FKR

model gives the formal definitions of unit,

relation, facet, interpretation, and organizes

facet terms into a lattice structure. In

contrast to the above set theory based

models, FKR can only map data items to a

single taxonomy and has no interactivity.

Faceted Lightweight Ontology proposed by

Giunchiglia et al. [30] is a typical

lightweight ontology. It has a rooted tree

structure where each node is associated with



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a natural language label. The labels of nodes

are organized according to certain

predefined patterns that capture different

aspects of items. This model gives the

definitions of category ontology, lightweight

ontology, and faceted lightweight ontology,

but does not provide interactive operations.

Organizing information into a hierarchical

category structure to facilitate information

seeking is a long standing research topic

[31]. Findings from eye tracking studies and

transaction log analysis demonstrated that a

substantial portion of users’ time was spent

on interactions with the result categories

[32]. On average, a user spent around 25 s

looking at the facets and 50 s looking at the

search results for an individual search.

According to a study by Kules and

Shneiderman [34]. Their findings showed

that the categorization offered an overview

of the result list and encouraged users to

explore more deeply and broadly. Users

were able to better assess search results and

felt more organized [34]. In health

information retrieval, Pratt [35] suggested

organizing results into categories based on

the United Medical Language System

(UMLS). Similar documents were

dynamically classified into groups in

accordance with a predefined terminology

model and a query model. The terminology

model consisted of the Medical Subjects

Headings (MeSH) and the semantic types in

the UMLS Semantic Network. The query

model provided information about the types

of the queries [35]. Based on these models, a

search system called DynaCat was

developed and its effectiveness was

evaluated in a user study. Fifteen patients

with breast cancer and their family members

participated in the study. The results showed

that, as compared to the traditional

document ranking list tool, users found

significantly more answers in a fixed

amount of time and felt more satisfied when

using DynaCat [36]. Later studies on

PubMed using MeSH browser [37] and a

subset of MEDLINE database using UMLS

Metathesaurus [38] also concluded the

helpfulness of facet category in users’ health

and biomedical information retrieval. For

exploratory search, it is important for the

search system to effectively support users’

navigation of the vast amount of retrieved

medical citations [39,40]

4 Proposed work

Data mining is an essential step of

knowledge discovery. In recent years it has

attracted great deal of interest in Information

industry [41]. Knowledge discovery process

consists of an iterative sequence of data

cleaning, data integration, data selection,

data mining pattern recognition and

knowledge presentation. In particulars, data

mining may accomplish class description,

association, classification, clustering,

prediction and time series analysis. Data

mining in contrast to traditional data

analysis is discovery driven [42].

In nowadays so many persons like illiterate

and literates suffering from Headache it is

not a small issue, it is growing up day by

day to generate a decease .So many types of

Headache here how to resolve problem and

some suggestions giving in result .The

different stages in the headache, and its

related suggestions like medication, doctors

suggestions their address and fee details has

discussed in the manuscript has given in this

result. The completed result was simulated

by the local database and result was satisfied

by the expected result.

Query Faceted Search

Faceted search is a technique for accessing

information organized according to a faceted

classification system, allowing users to

digest, analyze and navigate through

multidimensional data. It is widely used in

e-commerce and digital libraries [43].



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Faceted search is similar to query facet

extraction in that both of them use sets of

coordinate terms to represent different facets

of a query. However, most existing works

for faceted search are build on as specific

domain or predefined categories [44], while

query facet extraction does not restrict

queries in a specific domain, like products,

people, etc.

Query facet extraction is the problem of

finding query facets for a given query q

from available resources, such as web search

results. A query facet F t is a set of

coordinate terms, terms that are part of a

semantic set, which we call facet terms.

Query facets can be extracted from a variety

of different resources, such as a query log,

anchor text, taxonomy and social

folksonomy. In this work, we only focus on

extracting query facets from the top k web

search results 1 2 3, , ......, KD D D D D .We

intend to explore the use of other

information sources for this problem in

future work.

QDMiner extracts lists from free text,

HTML tags, and repeat regions contained in

the top search results, groups them into

clusters based on the items they contain,

then ranks the clusters and items based on

how the lists and items appear in the top

results. To summarize the information

contained in the query to find a list of

related queries. QDMiner, to automatically

mine query aspects by way of extracting and

grouping common lists from loose textual

content.

Algorithms

DDAL (Deduplication algorithm) algorithm

is proposed to detect and remove the

duplicate facets or items.

Architecture

The data backup stream from the computing

device is sent to the application aware

module in which chunks the data stream is

chunked into the various chunks depending

upon the type of the data that it belongs to.

Then the files are taken to similarity

function depending on the application or the

type of the file. Then the corresponding

similarity function is applied to the files. If

the similarity of the files defines to be

duplicate of the file already present in the

cloud backup, then the data will generated as

pairs and sent to the genetic programming

module for deduplication process.

5 Results & Discussion:

As per Cephalalgia study, The International

survey for Headache, one thousand two

hundred and seven migraine patients were

tested at The Headache Centre of Atlanta, a

clinical practice in the USA. The graph in

Fig.4. Shows the results of this study [45]

Fig.4. Headache triggers Vs people’s

percentage.

0

10

20

30

40

50

60

70

80

Stre

ss

Ho

rmo

ne

No

t ea

tin

g

We

ath

er

Sle

ep d

istu

rban

ces

Pe

rfu

me

/od

ou

r

Nec

k p

ain

Ligh

ts

Alc

oh

ol

Smo

ke

Sle

epin

g la

te

Hea

t

Foo

d

Headache Triggers in

percentage



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When a faceted search system processes a

query, its first task is to determine the set of

documents that satisfy the query constraints.

This set retrieval is straightforward and can

be accomplished efficiently using standard

inverted index techniques, such as those

described in [46]. The subsequent task,

however, is to show the facet values

available for refining the set of results. In

addition, faceted search applications often

show the counts associated with these

refinements.

The task of computing refinements is

significantly more demanding than that of

computing the set of results. One of the

health issue headache was taken as an

example to test the algorithm. The query

faceted result was simulated In ‘C’

language.

The complete result was displayed in the

terminal based on the query. The different

Types of Data Attributes, based on the

Query have given in the listed figures. Here

the result is explaining about the types of

headaches, reason for headache, and it is

suggesting about the medication, Doctors

address and fee details.

The Data Attributes has created in the Excel

which is interfaced with the local database.

When one data attribute is activated

automatically the related query will raise,

based on the attribute selection it will

fetches data or sub linked Data Attribute

from the local server.

Fig.5. Home button.

Fig .6. Types of Headaches.

Fig .7. Primary for Headaches.

Fig .8. Secondary headaches.



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Table.1. Medication for the headaches.

Define Symptoms Preventive Medication Progression

Migraine is an intense

pulsing from deep within

your head. This pain can

last for days. The

headache significantly

limits your ability to carry

out your daily routine.

Migraine is throbbing and

usually one-sided. People

with migraine headaches

are often sensitive to light

and sound. Nausea and

vomiting also usually

occur.

These Symptoms

before the headache

1.Starts flashing lights

2.Shimmering lights

3.Zigzag lines

4.Stars

5.Blind spots

1.Sumatriptan

2.Rizatriptan

3.Propranolo

4.Metoprolol 5.Topiramate 6n.Amitriptyline

1.Stroke,

2.Heart Disease,

3.High B.P,

4.Hearing

Problems,

5. Posttraumatic

Stress Disorder.

The simulated experimental result was

depicted in the Fig.5, Fig.6, Fig.7, Fig.8,

and the suggestible medication has given in

the Table.1.

Conclusion:

It can be concluded from the present

research that headache was the common

thing for many persons, which can happen in

different ways for all aged persons. The

research proposed methodology gives the

information about causes for the headache; it

can also give the brief information about the

medication for the headache and the details

of the doctor like fee structure, location of

the treatment.

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