The insurance domain has the large amount of data to be presented as useful information. But there are no systematic techniques to produce the information. So, we apply the data mining techniques to mine the data, related to customers, in order to provide the new mined information to the insurance company. Most of the insurance companies have the customer related information as a whole. Here clustering, one of the data mining techniques can be used to group the customer related information into different clusters. Each cluster represents the similar group of customers. For example, in our application, the customers are clustered according to their type of the plan, age, marital status, method of premium payment, monthly income in order to identify the potentiality of the policy holders and to improve the benefits of the plans. There are many attributes that can be used as parameters to cluster the customer related data. Some of the sample attributes are mentioned above. In this implementation the data is clustered by using policy number as a parameter with the help of the enhanced k-medoid clustering algorithm.

{"references": ["1.\tLance Parsons, Ehtesham Haque, Huan Liu, \"Evaluating Subspace Clustering Algorithms\", Supported in part by grants from Prop 301 (No. ECR A601) and CEINT, 2004. 2.\tMoh'd Belal Al- Zoubi, \"An Effective Clustering-Based Approach for Outlier Detection\", published in European Journal of Scientific Research ISSN 1450-216X Vol.28 No.2, pp.310-316, 2009. 3.\tBarry Senensky, Jonathan Polon, \"Dental Insurance Claims Identification of Atypical Claims Activity\", published in BSc, FSA, April 2007. 4.\tDilbag Singh, Pradeep Kumar, \"Conceptual Mapping Of Insurance Risk Management To Data Mining\" published in International Journal of Computer Applications (0975 \u2013 8887) Volume 39\u2013 No.2, February 2012 -13 . 5.\tV. Sree Hari Rao*, Murthy V. Jonnalagedda ,\"Insurance Dynamics \u2013 A Data Mining Approach For Customer Retention In Health Care Insurance Industry\" published in Bulgarian Academy Of Sciences Cybernetics And Information Technologies \u2022 Volume 12, No 1 Sofia \u2022 2012."]}