Abstract—Clustering technique is critically important step in data mining process. It is a multivariate procedure quite suitable for segmentation applications in the market forecasting and planning research. This research paper is a comprehensive report of k-means clustering technique and SPSS Tool to develop a real time and online system for a particular super market to predict sales in various annual seasonal cycles. The model developed was an intelligent tool which received inputs directly from sales data records and automatically updated segmentation statistics at the end of day’s business. The model was successfully implemented and tested over a period of three months. A total of n = 2138, customer, were tested for observations which were then divided into k = 4 similar groups. The classification was based on nearest mean. An ANOVA analysis was also carried out to test the stability of the clusters. The actual day to day sales statistics were compared with predicted statistics by the model. Results were quite encouraging and had shown high accuracy.
Index Terms—Cluster analysis, data mining, customer segmentation, ANOVA analysis.
Kishana R. Kashwan is with the Department of Electronics and Communication Engineering–PG, Sona College of Technology (An Autonomous Institution Affiliated to Anna University), TPT Road, Salem-636005, Tamil Nadu, India (e-mail: drkrkashwan@gmail.com, drkrkashwan@sonatech.ac.in). C. M. Velu is with the Department of CSE, Dattakala Group of Institutions, Swami Chincholi, Daund, Pune–413130, India (e-mail: cmvelu41@gmail.com).
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Cite:Kishana R. Kashwan and C. M. Velu, "Customer Segmentation Using Clustering and Data Mining Techniques," International Journal of Computer Theory and Engineering vol. 5, no. 6, pp. 856-861, 2013.
