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决策树在寿险企业客户流失分析中的应用 被引量:3

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摘要 保险业拥有众多有关客户的数据,正确地挖掘与分析隐含在这些数据中的知识,找出客户行为模式,可以有效地留住客户,向客户提供更好的保险产品与服务,从而在竞争中获胜。本文运用决策树的分析方法,结合某保险公司客户的实际数据,从理论和实证两个角度研究了客户流失分析,证明了决策树技术在寿险客户流失分析中的可行性。
出处 《现代商业》 2008年第20期138-138,共1页 Modern Business
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  • 1[1]Lee Wenke, Stolfo S J. Data mining approaches for intrusion detection. In: Proc the 7th USENIX Security Symposium, San Antonio, TX, 1998
  • 2[2]Lee Wenke, Stolfo S J, Mok K W. A data mining framework for building intrusion detection models. In: Proc the 1999 IEEE Symposium on Security and Privacy, Berkely, California, 1999. 120-132
  • 3[3]Lee Wenke. A data mining framework for constructing features and models for intrusion detection systems[Ph D dissertation]. Columbia University, 1999
  • 4[4]Paxson Vern. Bro: A system for detecting network intruders in real-time. In: Proc the 7th USENIX Security Symposium, San Antonio, TX, 1998
  • 5[5]Agrawal Rakesh, Srikant Ramakrishnan. Fast algorithms for mining association rules. In: Proc the 20th International Conference on Very Large Databases, Santiago, Chile, 1994
  • 6[6]Agrawal Rakesh, Srikant Ramakrishnan. Mining sequential patterns. IBM Almaden Research Center, San Jose, California:Research Report RJ 9910, 1994
  • 7[7]Chen M, Han J, Yu P. Data mining: An overview from database perspective. IEEE Trans Knowledge and Data Engineeing, 1996,8(6):866-883
  • 8ESTER M, KRIEGEL HP, SANDER J,et al. A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise[A]. Proc. of 2nd Int. Conf. on Knowledge Discovery and Data Mining (KDD-96)[C]. Portland, Oregon, 1996.
  • 9BECKMANN N. KRIEGEL HP, SCHNEIDER R,et al. The R^*-tree: An Efficient and Robust Access Method for Points and Rectangles[A]. Proc. ACM SIGMOD[C], 1990.322-331.
  • 10AHN HK, MAMOULIS N, WONG HM. A Survey on Multidimensional Access Methods[R]. Research report, Hong Kong University of Science and Technology, Hong Kong, 1997.

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