Customer Segmentation for Decision Support using Clustering and Association Rule based approaches
Journal Title: International Journal of Computer Science & Engineering Technology - Year 2012, Vol 3, Issue 11
Abstract
Key business areas that data mining techniques can be potentially applied to include business profitability, customer relationships, and business process efficieny. Customer Realtionship Management(CRM)has become a leading business strategy in highly competitive business environments.Clustering customers provides an in-depth understanding of their behavior. Clustering is one of the most important and useful technologies in data mining methods. Clustering is to group objects together, which is based on the difference of similarity on each object, and making highly homogeneity in the same cluster, or highly heterogeneity between each group. The scope of this paper to understand and predict the behavior of customers with behaviour segmentation methodology. The result of the study can support customer development by offering the right products to right customers and better targeting of product promotion campaigns. The policy holders claim dataset of health insurance company is taken for analysis. This behaviour segmentation methodology with clustering is applied in this chapter to predict distinct customer segments facilitating the development of customized new products and new offerings which better address the specific priorities and preferences of the customers. Apriori association rule performed on clusters of claim dataset gives the association among attributes in the claims dataset is derived from Clustering Based Association Rule Mining (CBARM) model. Association rule technique is applied on claim dataset to predict claim cost and the association among attributes that influences the claim cost of the policy holders.
Authors and Affiliations
S. Balaji , Dr. S. K. Srivatsa
Keywords
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