Clustering of Customers Based on Shopping Behavior and Employing Genetic Algorithms
Abstract
Clustering of customers is a vital case in marketing and customer relationship management. In traditional marketing, a market seller is categorized based on general characteristics like clients’ statistical information and their lifestyle features. However, this method seems unable to cope with today’s challenges. In this paper, we present a method for the classification of customers based on variables such as shopping cases and financial information related to the customers’ interactions. One measure of similarity was defined as clustering and clustering quality function was further defined. Genetic algorithms been used to ensure the accuracy of clustering.
Keywords:
classification, customers, shopping, genetic algorithmDownloads
References
S. Dibb, L. Simkin, The market segmentation workbook: target marketing for marketing managers, Routledge, London, 1996
A. Berson, S. Smith, K. Thearling, Building data mining applications for CRM, New York: McGraw-Hill, 2000
S. Wedel, W. Kamakura, Market segmentation: Conceptual and methodological foundations, Boston: Kluwer, 1997
T. P. Beane, D. M. Ennis, “Market segmentation: a review”, European Journal of Marketing, Vol. 21, No. 5, pp. 20–42, 1987 DOI: https://doi.org/10.1108/EUM0000000004695
K. Hammond, A. S. C. Ehrenberg, G. J. Goodhardt, “Market segmentation for competitive brands”, European Journal of Marketing, Vol. 30, No. 12, pp. 39–49, 1996 DOI: https://doi.org/10.1108/03090569610153291
R. J. Kuo, L. M. Ho, C. M. Hu, “Integration of self-organizing feature map and K-means algorithm for market segmentation”, Computers and Operations Research, Vol. 29, No. 11, pp. 1475–1493l, 2002 DOI: https://doi.org/10.1016/S0305-0548(01)00043-0
R. G. Drozdenko, P. D. Drake, Optimal database marketing: Strategy, development, and data mining, London, Sage, 2002
C. D. Manning, H. Schutze, Foundations of statistical natural language processing, Cambridge, MA, MIT Press, 1999
H. C. Romesburg, Clustering analysis for researchers, Belmont Lifetime Learning Publications, 1984
R. Agrawal, R. Srikant, “Fast algorithms for mining association rules”, 20th International Conference on Very Large Databases, pp. 487–499, 1994
H. Mannila, "Database methods for data mining", 4th International Conference on Knowledge Discovery and Data Mining, New York, 1998
J. H. Holland, Adaptation in natural and artificial systems, Ann Arbor, MI: The University of Michigan Press, 1975
J. MacQueen, “Some methods for classification and analysis of multivariate observations”, 5th Conference on Mathematical Statistics and Probability, Vol. 1, pp. 281–297, 1967
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