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A Study on Customer Segmentation for E-Commerce Using the Generalized Association Rules and Decision Tree

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DOI: 10.4236/ajibm.2015.512078    4,819 Downloads   5,400 Views   Citations
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ABSTRACT

With the rapid development of e-commerce, e-commerce is becoming more and more competitive. How to improve customer loyalty, attract more new customers, and expand the market effectively, it is very important for the e-commerce enterprise. In this paper, a comprehensive model is proposed, which is based on generalized association rules and decision tree technology. The model is used for customer segmentation of e-commerce website. It can help e-commerce companies understand customers, support decision-making, so as to provide customers with more targeted services.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

Ma, H. (2015) A Study on Customer Segmentation for E-Commerce Using the Generalized Association Rules and Decision Tree. American Journal of Industrial and Business Management, 5, 813-818. doi: 10.4236/ajibm.2015.512078.

References

[1] Silversteinc, B.M. (1997) Beyond Market Basket: Generalizing Association Rules to Correlations. Proceedings of the 1997 ACM SIGMOD International Conference on Management of Data (SIGMOD97), Tucson, Date, 265-276.
[2] Berson, A. and Smith, S. (2001) Building Data Mining Applications for CRM. Posts & Telecom Press, Beijin.
[3] Alfredo, V.A. (2000) Methodology for the Characterization of Business-to-Consumer E-Commerce. John Moores University, Liverpool.
[4] Tsiptsis, K. and Chorianopoulos, A. (2010) Data Mining Techniques in CRM: Inside Customer Segmentation. John Wiley and Sons, Hoboken.
http://dx.doi.org/10.1002/9780470685815
[5] Rygielski, C., Wang, J.C. and Yen, D.C. (2002) Data Mining Techniques for Customer Relationship Management. Technology in Society, 24, 483-502.
http://dx.doi.org/10.1016/S0160-791X(02)00038-6
[6] Han, J.W. (2002) Data Mining Concepts and Techniques. Machinery Industry Press, Beijing.

  
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