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Journal of Theoretical and Applied Information Technology

© 2005 - 2015 JATIT & LLS. All rights reserved

ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195

JOURNAL OF THEORETICAL AND APPLIED INFORMATION TECHNOLOGY

EDITORIAL COMMITTEE

NIAZ AHMAD (Chief Editor) Professor, FCE, MOE, H-9 Islamabad PAKISTAN

SHAHBAZ GHAYYUR (Co- Chief Editor) Assistant Professor, DCS, FBAS, International Islamic University Islamabad, PAKISTAN SAEED ULLAH (Associate Editor) Assistant Professor, DCS, Federal Urdu University of Arts, Science & Technology Islamabad, PAKSITAN

MADIHA AZEEM (Associate Editor) Journal of Theoretical and Applied Information Technology, Islamabad. PAKISTAN

SALEHA SAMAR (Managing Editor) Journal of Theoretical and Applied Information Technology, Islamabad. PAKISTAN KAREEM ULLAH (Managing Editor) Journal of Theoretical and Applied Information Technology, Islamabad. PAKISTAN

SHAHZAD A. KHAN Lecturer IMCB, FDE Islamabad, PAKISTAN (Managing Editor/Linguists & In-charge Publishing) Journal of Theoretical and Applied Information Technology, Islamabad. PAKISTAN

August 2015. Vol. 78 No.3 .

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JOURNAL OF THEORETICAL AND APPLIED INFORMATION TECHNOLOGY REGIONAL ADVISORY PANEL

Dr. SIKANDAR HAYAT KHIYAL Professor &Chairman DCS& DSE, Fatima Jinnah Women University, Rawalpindi, PAKISTAN Dr. MUHAMMAD SHER Professor &Chairman DCS, FBAS, International Islamic University Islamabad, PAKISTAN Dr. ABDUL AZIZ Professor of Computer Science, University of Central Punjab, PAKISTAN Dr. M. UMER KHAN Asst. Professor Department of Mechatronics, Air University Islamabad, PAKISTAN Dr. KHALID HUSSAIN USMANI Asst. Professor Department of Computer Science, Arid Agriculture University, Rawalpindi, PAKISTAN


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JOURNAL OF THEORETICAL AND APPLIED INFORMATION TECHNOLOGY

EDITORIAL ADVISORY BOARD


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Dr. CHRISTEL BAIER Technical University Dresden, GERMANY

Dr KHAIRUDDIN BIN OMAR UniversitiKebangsaanMalysia, 43600 Bangi Selangor Darul-Ehsan, MALYSIA

Dr. YUSUF PISAN University of Technology, Sydney, AUSTRALIA

Dr. S. KARTHIKEYAN Department of Electronics and Computer Engineering, Caledonian College of Engineering, OMAN (University College with Glascow University, Scotland, UK)

DR. YUXIN MAO School Of Computer & Information Engineering Zhejiang Gongshang University, CHINA Dr. ZARINA SHUKUR FakultiTeknologidanSainsMaklumat, University Kebangsaan MALYSIA

Dr. NOR AZAN MAT ZIN Faculty of Information Science & Technology, National University of MALYSIA

Dr. R.PONALAGUSAMY National Institute of Technology, Tiruchirappalli, Tamil Nadu, INDIA

Dr. MOHAMMAD TENGKU SEMBOK Universiti Kebangsaan MALYSIA

Dr. PRABHAT K. MAHANTI University of New Brunswick, Saint John, New Brunswick, CANADA

Dr. NITIN UPADHYAY Birla Institute of Technology and Science (BITS), Pilani-Goa Campus, INDIA

Dr. S.S.RIAZ AHAMED Mohamed Sathak Engineering College, Kilakarai, &Sathak Institute of Technology, Ramanathapuram , Tamilnadu, INDIA

Dr. A. SERMET ANAGÜN Eskisehir Osmangazi University, Industrial Engineering Department, Bademlik Campus, 26030 Eskisehir, TURKEY.

Dr. YACINE LAFIFI Department of Computer Science, University of Guelma, BP 401, Guelma 24000, ALGERIA.

Dr. CHRISTOS GRECOS School Of Computing, Engineering And Physical Sciences University Of Central Lancashire. UNITED KINGDOM

Dr. JAYANTHI RANJAN Institute of Management Technology Raj Nagar, Ghaziabad, Uttar Pradesh, INDIA

Dr. ADEL M. ALIMI National Engineering School of Sfax (ENIS), University of SFAX, TUNISIA

Dr. RAKESH DUBE Professor & Head, RKG Institute of Technology, Ghaziabad, UP, INDIA

Dr. ADEL MERABET Department of Electrical & Computer Engineering, Dalhousie University, Halifax, CANADA

Dr. HEMRAJ SAINI CE&IT Department, Higher Institute of Electronics, BaniWalid. LIBYA

Dr. MAUMITA BHATTACHARYA SOBIT, Charles Sturt University Albury - 2640, NSW, AUSTRALIA





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Dr. SEIFEDINE KADRY Lebanese International University, LEBONON

Dr. AIJUAN DONG Department of Computer Science Hood College Frederick, MD 21701. USA

Dr. ZURIATI AHMAD ZUKARNAIN University Putra Malaysia, MALAYSIA

Dr. HEMRAJ SAINI Higher Institute of Electronic, BaniWalid LIBYA

Dr. CHELLALI BENACHAIBA University of Bechar, ALGERIA

Dr. MOHD NAZRI ISMAIL University of Kuala Lumpur (UniKL) MALYSIA

Dr. VITUS SAI WA LAM The University of Hong Kong, CHINA

Dr. WITCHA CHIMPHLEE SuanDusitRajabhat University, Bangkok, THAILAND

Dr. SIDDHIVINAYAK KULKARNI University of Ballarat, Ballarat, AUSTRALIA

Dr. S. KARTHIKEYAN Caledonian College of Engineering, OMAN

Dr. DRAGAN R. MILIVOJEVIĆ Mining and Metallurgy Institute BorZelenibulevar 35, 19210 Bor, SERBIA

Dr. E. SREENIVASA REDDY Principal - VasireddyVenkatadri Institute of Technology, Guntur, A.P., INDIA

Dr OUSMANE THIARE Gaston Berger University, Department of Computer Science, UFR S.A.T, BP 234 Saint- Louis SENEGAL

Dr. SANTOSH DHONDOPANT KHAMITKAR RamanandTeerthMarathwada University, Nanded. Maharashtra 431605, INDIA

Dr. M. IQBAL SARIPAN (MIEEE, MInstP, Member IAENG, GradBEM) Dept. of Computer and Communication Systems Engineering, Faculty of Engineering, Universiti Putra MALAYSIA

Dr. E. SREENIVASA REDDY Principal - VasireddyVenkatadri Institute of Technology, Guntur, A.P., INDIA

Dr. T.C.MANJUNATH, Professor & Head of the Dept., Electronicis& Communication Engg. Dept, New Horizon College of Engg., Bangalore-560087, Karnataka, INDIA.

Dr. SIDDHIVINAYAK KULKARNI Graduate School of Information Technology and Mathematics University of Ballart AUSTRALIA

Dr. SIKANDAR HAYAT KHIYAL Professor & Chairman DCS& DSE, Fatima Jinnah Women University, Rawalpindi, PAKISTAN

Dr. MUHAMMAD SHER Professor & Chairman DCS, FBAS, International Islamic University Islamabad, PAKISTAN

Dr. ABDUL AZIZ Professor of Computer Science, University of Central Punjab, PAKISTAN

Dr. M. UMER KHAN Asst. Professor Department of Mechatronics, Air University Islamabad, PAKISTAN




Elite Panel Members Have A Decision Weight Equivalent of Two Referees (Internal OR External).

The Expertise Of Editorial Board Members Are Also Called In For Settling Refereed Conflict About

August 2015. Vol. 78 No.3

Acceptance/Rejection And Their Opinion Is Considered As Final.

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Dr. RIKTESH SRIVASTAVA Assistant Professor, Information Systems Skyline University College P O Box 1797, Sharjah, UAE

Dr. BONNY BANERJEE PhD in Computer Science and Engineering, The Ohio State University, Columbus, OH, USA Senior Scientist Audigence, FL, USA

PROFESSOR NICKOLAS S. SAPIDIS DME, University of Western Macedonia Kozani GR-50100, GREECE.

Dr. NAZRI BIN MOHD NAWI Software Engineering Department, Faculty of Science Computer Information Technology, Universiti Tun Hussein Onn MALAYSIA

Dr. JOHN BABALOLA OLADOSU Ladoke Akintola University of Technology, Ogbomoso, NIGERIA

Dr. ABDELLAH IDRISSI Department of Computer Science, Faculty of Science, Mohammed V University - Agdal, Rabat, MOROCCO

Dr. AMIT CHAUDHRY University Institute of Engineering and Technology, Panjab University, Sector-25, Chandigarh, INDIA

Dr. ASHRAF IMAM Aligarh Muslim University, Aligarh-INDIA

Dr. MOHAMMED ALI HUSSAIN Dept. of Computer Science & Engineering, Sri Sai Madhavi Institute of Science & Technology, Mallampudi, Rajahmundry, A.P, INDIA

Dr. KHALID HUSSAIN USMANI Asst. Professor Department of Computer Science, Arid Agriculture University, Rawalpindi, PAKISTAN

Dr. GUFRAN AHAMD ANSARI Qassim University, College of Computer Science, Ministry of Higher Education, Qassim University, KINGDOM OF SAUDI ARABIA

Dr. Defa Hu School of Information, Hunan University of Commerce Changsha 410205, Hunan, P. R. of China




PREFACE

Journal of Theoretical and Applied Information Technology (JATIT) published since 2005 (E-ISSN 1817- 3195 / ISSN 1992-8645) is an International refereed research publishing journal with a focused aim of promoting and publishing original high quality research dealing with theoretical and scientific aspects in all disciplines of Information Technology. JATIT is an international scientific research journal focusing on issues in information technology research. A large number of manuscript inflows, reflects its popularity and the trust of world's research community. JATIT is indexed with various organizations and is now published on monthly basis.

All technical or research papers and research results submitted to JATIT should be original in nature, never previously published in any journal or undergoing such process across the globe. All the submissions will be peer-reviewed by the panel of experts associated with JATIT. Submitted papers should meet the internationally accepted criteria and manuscripts should follow the style of the journal for the purpose of both reviewing and editing. All of its articles also appear online as per policy of JATIT

Journal of Theoretical and Applied Information Technology receives papers in continuous flow and we will consider articles from a wide range of Information Technology disciplines encompassing the most basic research to the most innovative technologies. Please submit your papers electronically to our submission system at http://jatit.org/submit_paper.php in an MSWord, Pdf or compatible format so that they may be evaluated for publication in the upcoming issue. This journal uses a blinded review process; please remember to include all your personal identifiable information in the manuscript before submitting it for review, we will edit the necessary information at our side. Submissions to JATIT should be full research / review papers (properly indicated below main title). It is the sole responsibility of the submitting authors to make sure that the submitted manuscript is not in process of publication anywhere in any conference/journal across the globe, nor part or whole of it is copied from any source. The review process may take anywhere from five days to two months depending on the response time to referees. Authors will be informed about the updated status via e-mail as soon as we receive the evaluation results. After submission of publication dues for accepted manuscripts a publication slot will be allocated to your manuscript for its publication in upcoming monthly issues of JATIT.

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ABSTRACTING & INDEXING

Journal of Theoretical and Applied Information Technology Islamabad Pakistan is focused, double blind peer reviewed journal that is now being published monthly and is published by Asian Research Publishing Network and is Indexed / Abstracted by the following International Agencies and institutions. JATIT has been regularly published since 2005 and now has a well reputed international standing and invites contributions from researchers, scientists, and practitioners from all over the world.

*- Ulrich's Periodicals Directory *- DataBase systems and Logic Programming (DBLP) *- EBSCO Publishing USA *- Directory of Open Access Journals (DOAJ) *- Google & Google Scholar Journals *- The Index of Information Systems Journals *- Information Technology Resources Collection *- ZDNet Australia *- NLM Catalog *- Computing Research and Education Association of Australasia *- CiteSeer *- Elsevier *- SCOPUS *- Engineering Village *- TOC Premier

****************** Feel free to suggest JATIT to any Indexing & Abstracting Services which are appropriate to its scope

TM

August 2015. Vol. 78 No.3

*- Computer Science Journals *- Computers and Applied Sciences Complete *- N|W Switzerland *- Microsoft Academic Search *- Cabell Publishing *-OpenJgate *- INSPEC *- IAOR Palgrave Macmillan

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Journal of Theoretical and Applied Information Technology31st August 2015. Vol.78. No.3

© 2005 - 2015 JATIT & LLS. All rights reserved.


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CUSTOMER SEGMENTATION THROUGHFUZZY C-MEANS AND FUZZY RFM METHOD

NI PUTU PUTRI YULIARI1,I KETUT GEDE DARMA PUTRA2, NI KADEK DWI RUSJAYANTI3

Department Of Information Technology, Engineering Faculty in Udayana UniversityBukit Jimbaran, Bali, Indonesia, Telp. +62361703315

E-mail: [email protected],[email protected],[email protected]

ABSTRACT

This research aims for finding the potential customer use data transaction. This causes, the company isdifficult to arrange customers who have high and low loyalty and this research have a application forcustomer segmentation to help analyzing transaction data with Fuzzy C-Means for clustering and FuzzyRFM for identify the customers. Softwares used to conduct this experiment are Microsoft SQL Server tosaving the database and Matlab as the tools. The results of this segmentation for four experiments are twoclasses. Its has superstar I and Occasional H for each number cluster and then for the best number of clusterfor this experiments are two clusters according MPC method.

Keywords: Customer segmentation, Clustering, Fuzzy c-means, Fuzzy RFM, MPC (Modified PartitionCoefficient)

1. INTRODUCTIONThe company is difficult to arrange

customers who have high and low loyalty. This iscaused of data transaction growth fast and limitedability to segmenting with manual computation.Amount of data from furniture company hasimportant information to segmenting, the processof finding in a set of data called data mining[1].Data mining is used to give services to customersbased on views or insights of customers with CRMstrategy. Every relation with customers can make abenefit to the company. Profitable relationship isdone by analyzing data transaction of customers.Because of that marketing is important to dividingcustomers[2]. Volume data is continues to grow andcan not be analysis with manual[1]. The applicationof data mining can help in analyzing customers todetermine level of loyalty customer.

Effective segmentation leads tocompetitive advantage, recognition andexploitation of new market opportunities, selectionof the appropriate target market, enhanceddifferentation and positioning, and increasedprofitability. Despite the appealing strategic andtactical benefit of market segmentation, clusteranalysis remain the most favoured method.[6] Thebasic of idea of cluster analisys is to divide aheterogeneous customers market into homogeneoussub-grups[9]. But, some information is inevitablylost when object are grouped. Information loss is

not problematic but it can result in the wrongconclusions[8]. Hence, there is no succesfulsegmentation without an appropriate clusteringalgorithm[7].

Therefore, this research have a applicationfor customer segmentation to help analyzingtransaction data in a furniture company, theapplication is developing method of Fuzzy C-Means and Fuzzy RFM. Software used to conductthis experiment is Microsoft SQL Server for savingdatabase and Matlab as the tools. This applicationused fuzzy clustering algorithm with Fuzzy C-Means method, the algorithm have been selectedbecause this method can make data grouped by thecluster. Fuzzy RFM (Recency, frequency,monetary) method used to choose customer withhigh or low loyalty from the result data of Fuzzy C-Means method. Fuzzy RFM can determinecustomer to the class with level loyalty their have.

2. CUSTOMER SEGMENTATION

Segmentation is process for dividedcustomers to the some cluster with category of theloyalty customer for build the market strategy. TheCharacteristic of segmentation are made bybussiness rule. The clustering algorithm can beanalize characteristic of data, cluster identificationand result of monitoring data model. The model ofoperator data mining are build for searching the




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good cluster and characteristic distinctly.Figure 1 explaining about system process forcustomer segmentation, the input of this system isdatabase from the company. Database from thecompany was choosed use data preparation process,data will divided to third group with Fuzzy RFMparameter. Data with paramater will be clusteringwith Fuzzy C-Means method and then apply MPCmethod for validity cluster. Output from thisapplication is class category of the customer.

Figure 1: System Process

3. CLUSTERING

The RFM method used for determinevariable of measuring purchase products bycustomers. Variable can determine as recency,frequency and monetary[3].

a. Recency is range time (day, month, year) fromend transaction until this time by customerspurchase.

b. Frequency is transaction total or transactionaverage in once period.

c. Monetary is average cost total of customers inonce period.

Segmentation cluster in the retail company dividedby six characteristic with RFM values ofcustomers[2].

Table 1:Customer Characteristic with RFM Values.

Customer Class Description

Superstar a. Customers with high loyaltyb. High valuesc. High frequency valuesd. Highest transaction

Golden Customer a. Second high valuesb. High frequency valuesc. Standard transaction values

Typical Customer a. Have standard value andtransaction values

Occational Customer a. Second of the last frequencyvalues after dormant customer

b. Lowest recencyc. Highest transaction

Everyday Shopper a. Have raising transactionb. Low transactionc. Have value with middle until

low scale

Dormant Customer a. Lowest frequency and valueb. Lowest recency

Attribute distribution base on RFM willshow in Table 2.Table 2: Domain Value RFMAtribute Linguistic

variableDomain Value

Recency Long Time AgoRather LongerRecently

0 ≤ r < Max_r1 dayMax_r1 day< r < Max_r2dayMax_r2 day < r

Frequency SeldomRather FrequentOftenVery Often

0 ≤ f < Max_f1 transactionMax_f1 transaction < f <Max_f2 transactionMax_f2 transaction <f<Max_f3 transactionMax_f3 transaction < f

Monetary Very LowLowRather LowRather HighHighVery High

0 ≤ m < Max_m1 RupiahIDR Max_m1 < m < IDRMax_m2IDR Max_m2 < m < IDRMax_m3IDR Max_m3 < m < IDRMax_m4IDR Max_m4 < m < IDRMax_m5IDR Max_m5 < m

Fuzzy RFM used trapezoid graph for dispartthe domain value. The graph of domain value fromfuzzy RFM will show in figure 2.

Figure 2a : Fuzzy RFM Recency

Figure 2b : Fuzzy RFM Frequency




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Figure 2c : Fuzzy RFM Recency

Customer segmentation process will dowith computing membership degree of the centroidfrom every cluster with all class from fuzzy model.Its computing used equation from zumstein[11]

(1)Explanation:µA = Degree of membership for every classµ i = Degree of membership for every linguistic

variable in Fuzzy RFMA = Class in RFM Modeli = Linguistic Variablex = Centroidγ = Gamma, using value 0,5

Table 2 explain about limit of class forcustomer segmentation as superstar, golden,typical, occasional, everyday and dormant. TheFCM approach has been applied to data clustering.Then for validation test of data using MPC method,its has for make sure the best number of clusters.The algorithm of MPC method is[10]:

(2)The C value is the centroid and then

MPC(c) is index value of MPC when cluster have cvalue.

4. RESULT AND DISSCUSION

4.1 Arsithecture Data AnalysisArsithecture data for customer

segmentation divided to 3 part which are dataselection, preprocessing and transformation. Dataselection is using transaction data of furniturecompany. Preprocessing step base on RFM methodused customerID, order date and unit price fromdatabase. And the final step is transformation datato RFM.

Figure 3 : Step of Architechture Data Selection

Matlab R2014b application has applied forimplement FCM method. The ODBC is used formake relation between Matlab and SQL server. IfMatlab and SQL server has a relation then theselection data can do based RFM method.

Figure 4 : RFM Data in 3D Graphic

Figure 4 explaining about data after executeto RFM and the graphic have information aboutdissemination data of the company. User can inputtotal cluster, weight and maximum iteration.

4.2 ExperimentIn this section, the experiment is taken to

demonstrate with two until five clusters using sameweight and maximal iteration, and then weimplement FCM to classify the customers. Thefigures 5 are about dissemination of data base ontotal cluster.




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(a)

(b)

(c)

(d)

Figure 5: Dissemination Data Graph (a) 2 Clusters, (b)3 Clusters, (d) 4 Clusters, (e) 5 Clusters

The result of the clustering process willused to find class of customer with Fuzzy RFMmethod, before finding the class use Fuzzy RFM.We should to implement MPC method forvalidition cluster to make sure the result of theclustering is right. We have the best number ofcluster according MPC method is two clusters.

Figure 7: Cluster Comparison

The best value for number of cluster (closeto one in range 0-1) with Fuzzy RFM method, itmeans quality of the cluster is more good forclustering data. The comparison of number ofcluster with MPC Method showed in figure 7 whichare 2, 3, 4 and 5 clusters and the best result is twoclusters with value of validation test is closed toone. This comparison aims is for knowing numberof cluster with good result for dissemination data.

An error in the usage of cluster numberwill cause an error in class identification and willaffect the marketing strategy of a product in thecompany which will cause loss in company profit.

The best cluster number from MPCmethod is two clusters. The implement of FCMmethod for two clusters with 38 iterationsimprovement and the minimal objective value is0.3855 will shown in the Table 3. Take customer925 as an example, the membership of belonging tocluster 1 is 0,9999, while cluster 2 (0,0001) is lowerthan cluster 1. As a result, if we classify customerswith according to highestvalues of membership, thecustomers 925, 687, 902, 710, 879, 733, 856, 779,and 802 in the same class. While customer 23 incluster 2.




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Table 3: The Classification Result Using FCM with 2Number Cluster

CustomerID Cluster 1 Cluster 2Classification

Result925 0.9999 0.0001 1.000023 0.1029 0.8971 2.0000687 0.9303 0.0697 1.0000902 0.9994 0.0006 1.0000710 0.9994 0.0006 1.0000879 0.9971 0.0029 1.0000733 0.9999 0.0001 1.0000856 0.9996 0.0004 1.0000779 0.9995 0.0005 1.0000802 0.9998 0.0002 1.0000

In this case, use the value of cluster centerfor compared with Fuzzy RFM Method. The clustercenter of two clusters shown in Table 4.

Table 4: Value of the Cluster CenterClass Code R F M Score

K26 1 1 0.9641 0.9819K30 1 1 1 1

In the table 4, class code K30 is code foroccasional H class and K26 for superstar I.Superstar customer is the customer with highloyalty, and the customer with Occasional class haslow frequency. So, in this research we have twocustomer characters that is high loyalty (Superstar)and low frequency but highest transaction(Occasional).

Figure 6: The Result of Customer Identified by FuzzyRFM Method

This research has two result, that is classof the customers and the best number of clusters.the application result can knowing potentialcustomer from class their have. Customers withincluded to cluster 1 will have Superstar class, andcluster 2 is Occasional class. From the result, thecompany can make a decision for subjected theircustomers. For the next experiment accordingFuzzy C-Means and Fuzzy RFM method should usetwo clusters.

5. CONCLUSIONSThe customer segmentation application is

developing method of Fuzzy C-Means and FuzzyRFM with data transaction. Its has can build clusterwith superstar customers class from comparingmembership degree of the centroid with the class ofFuzzy RFM Method. The result of four experimentwith same weight and maximum iteration have twoclass dominant which are superstar I andOccasional H. The cluster validity test with MPC(Modified Partition Coefficient) have the bestnumber of cluster for FCM is two clusters.

6. FURTHER RESEARCH DIRECTION

The experimental results supported theusefulness of the proposed methodology. In thefuture, other clustering methods can be fuzzified insimilar ways for the same purpose and not onlyfocus on customer segmentation but alsodetermine of the marketing target.

REFRENCES:

[1] Betalya. Konsep Data Mining dan KnowledgeDiscovery in Database. UniversitasGunadarma. 2009

[2] Tsiptsis Antonios Chorianopoulos. DataMining Techniques in CRM: Inside CustomerSegmentation. NewCaledonia: Antony RoweLtd, Chippenham, Wiltshire. 2009

[3] Chen. Understanding Customer RelationshipManagement (Crm) People, Process AndTechnology. Vol. 9 No. 5, 2003

[4] Chapman, P., Clinton, J., Kerber, R., Khabaza,T., Reinartz, T., Shearer, C., Wirth, R. 2000.CRISP-DM 1.0 : Step-by-Step Data MiningGuide. 2000

[5] Ching-hsue, Cheng. Classifying thesegmentation of customer value via RFMmodel and RS theory. Expert Systems withApplications 36 (2009) 4176–4184.

[6] Dolnicar, S., Crouch, G. I., Devinney, T.,Huybers, T., Louviere, J., and Oppewal, H.Tourism and discretionary income allocationheterogeneity among households. TourismManagement, 2009, 29(1):44–52.

[7] Dolnicar, S. Using cluster analysis for marketsegmentation–typical misconceptions,established methodological weaknesses andsome recommendations for improvement.




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Australasian Journal of Market Research,2003, 11(2):5–12.

[8] Franke, N., Reisinger, H., and Hoppe, D.Remaining within–cluster heterogeneity: ameta–analysis of the “dark–side” of clusteringmethods. Journal of Marketing Management,2009, 25(3/4):273–293.

[9] Punj, G. and Stewart, D. W. Cluster analysis inmarketing research: review and suggestionsfor application. Journal of MarketingResearch, 1983,20(2):138–148.

[10] Nugraheni, Yohana. Data mining denganmetode fuzzy untuk customer relationshipmanangement pada perusahaan retail.Denpasar : Universitas Udayana. 2011

[11] Zumstein, D. Customer PerformanceMeasurement : Analysis of the Benefit of aFuzzy Classification Approach in CustomerRelationship Management. Switzerland :University of Fribourg. 2007

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