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Customer Segmentation: Using a Comparative Case of Clustering Algorithms

Anjana K. Mahanta, Amar J. Singh, Th. Shanta Kumar

Abstract


Customer analysis is done by all businesses in some way or the other. It is the process of understanding who our customers are, their location and what make them to buy our products and services. Recent developments in the fields of business investment, scientific research and information technology have resulted in the collection of massive data which becomes highly useful in finding certain patterns governing the data source. Customer segmentation is a way to have more targeted communication with the customers. The process of segmentation describes the characteristics of the customer groups within the data. Clustering algorithms are popular in finding hidden patterns and information from such repository of data. This paper presents how different clustering algorithms can be compared and the optimal one can be selected for the purpose of customer segmentation for a shop. The information extracted can be used to satisfy the needs of the key customers and also helps in making strategic decision as to how the business can be expanded. This paper compares the four clustering algorithms: K-means, K-medoid, Fuzzy C-means and Gustafson-kessel. The best one is selected and optimal number of clusters is found out and applied to that algorithm for customer segmentation.

Keywords


Clustering, Customer Segmentation, Fuzzy C-Means, Gustafson-Kessel, K-Means, K-Medoids

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References


J. Ahola and E. Rinta-Runsala, “Data mining case studies in customer profiling”. Research report TTE1-2001-29, VTT Information Technology, Dec 2001.

K. S. Al-Sultan and M. M. Khan, “Computational experience on four algorithms for hard clustering problem,” Pattern Recogn. Lett. 17, 3, pp. 295-308, 1996.

J. L. Amat, “Using reporting and data mining techniques to improve knowledge of subscribers; Applications to customer profiling and fraud management,” J. Telecommun. Inform. Technol., no. 3, pp. 11-16, 2002.

B. Balasko, J. Abonyi and B. Feil, “Fuzzy Clustering and Data Analysis Toolbox For Use with Matlab”, 2006.

M. J. A. Berry and G. S. Linoff , “Data Mining Techniques for Marketing, Sales and Customer Relationship management”, Indiana, 2004.

M. Chau, R. Cheng and B. Kao, “Uncertain Data Mining: A New Research Direction”, 2005. Retrieved July 16, 2008, from www.business.hku.hk/~mchau/papers/ Uncertain Data Mining_WSA.pdf.

E. Cox, “Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration”. Elsevier Inc, 2005.

E. Gustafson and W. Kessel, “Fuzzy clustering with a fuzzy covariance matrix”, In Proc. of IEEE CDC, 1979.

U. Fayyad and R. Uthurusamy, “Data mining and knowledge discovery in databases”. Communications of ACM, 39, 24–27, 1996.

F. E. Giha, Y. P. Singh and H. T. Ewe, “Customer Profiling and Segmentation based on Association Rule Mining Technique”, Proceedings of Software, Engineering and Application, no. 397, 2003.

J. Han and M. Kamber, “Data Mining Concepts and Techniques”, Elsevier, India, 2003.

F. Hoppner, R. Kruse, F. Klawonn and T. Runkler, “Fuzzy Cluster Analysis: Methods for Classification, Data Analysis and Image Recognition”. Wiley, 1990.

A. K. Jain and P. J. Flynn, “Image segmentation using clustering”. In Advances in Image Understanding : A Festschrift for Azriel Rosendeld, N. Ahuja and K. Boyer, Eds, IEEE Press, Piscataway, NJ, 65-83, 1996.

S. Jansen, “Customer Segmentation and Customer Profiling for a Mobile Telecommunications Company Based on Usage Behavior”. Retrieved October 16, 2008, from http://www.personeel.unimaas.nl/Westra/PhDMa Bateaching/GraduationStudents/StephanJansen2007/Stephan_Jansen2007.pdf

K. Mali, “Clustering and its validation in a symbolic framework”. Patt. Recogn. Lett., vol. 24, pp. 2367-2376, 2003.

S. K. Mishra and V. V. Raghavan, “An Empirical study of the performance of heuristic methods for clustering”. In Pattern Recognition in Practice, E.S. Gelsema and L.N. Kanal, Eds., pp. 425-436, 1994.

E. W. T. Ngai, Li. Xiu and D. C. K. Chau, “Application of data mining techniques in customer relationship management: A literature review and classification,” Expert Systems with Applications, 36, pp 2592–2602, Elsevier, 2008.

K. Pal and P. Mitra, “Data Mining in Soft Computing Framework: A Survey. IEEE transactions on neural networks”, vol. 13, no.1, 2002.

Y. Peng, G. Kou and Z. Chen, “Recent trends in Data Mining (DM): Document Clustering of DM Publications,” Service Systems and Service Management, International Conference on Data Management. pp 1653 – 1659, 2002.

A. K. Pujari, “Data Mining Techniques,” University Press (India) Private Limited, 2001.

P. N. Tan, M. Steinbach and V. Kumar, “Introduction to Data Mining”, Addison Wesley, 2005.

K. Teknomo, “K-Means Clustering Tutorials,” Retrieved October 19, 2009, from http:people.revoledu.com karditutorialkMean

P. Verhoef, P. Spring, J. Hoekstra and P. Lee, “The commercial use of segmentation and predictive modeling techniques for database marketing in the Netherlands”. Decis. Supp. Syst., vol. 34, pp. 471-481, 2002.

R. C. T. Lee, “Cluster Analysis and its applications,” In Advances in Information Systems Science, J. T. Tou Ed. Plenum Press, New York, 1981.

X. Y. Wang and J. M. Garibaldi, “A Comparison of Fuzzy and Non-Fuzzy Clustering Techniques in Cancer Diagnosis”, International Conference in Computational Intelligence in Medicine and Healthcare, Lisbon, Portugal, 2005


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