Using the Clustering Algorithms and Rule-based of Data Mining to Identify Affecting Factors in the Profit and Loss of Third Party Insurance, Insurance Company Auto

Abstract

Background/Objectives: Insurance data analysis can be considered as a way of losses reduction by using data mining. It uses the machine learning, pattern recognition and data base theory for discovering the unknown knowledge. Methods/Statistical Analysis: In this paper, information of 2011, third party insurance of Iran insurance company auto has analyzed in Kohgiluyeh and Boyer Ahmad by using the data mining method. Findings: The results show that using clustering algorithms with acceptable clusters will be able to provide a model to identify affecting factors and to determine the effect of them in the profit and loss of auto third party insurance. Applications/Improvements: The algorithm of K-Means has formed the best clustering with 9 clusters that have relatively good quality. It means that has been able to maximize the distance between the cluster and minimize the within cluster distance.