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基于K-means算法的中国商业银行零售业务顾客行为细分策略 被引量:1

Segmentation Strategy on Customer Behavior of Commercial Bank Retail Business in China Based on K-means Arithmetic
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摘要 在分析中国商业银行零售业务现状和顾客行为分析问题的基础上,结合K-m eans算法和顾客行为细分方法,提出基于K-m eans算法的中国零售业务的顾客行为细分模型,并结合中国商业银行的个案,进行了我国商业银行零售业务顾客行为细分的实证研究。 On the basis of analyzing the status of commercial bank retail business in China and the problems of the analysis of customer behavior, this paper combined the K-means arithmetic and customer behavior method and put forward customer behavior segmentation model of commercial bank retail business in China based on K-means Arithmetic. At the same time, combining the specific case of a Chinese commercial bank, it did the empirical study on customer behavior segmentation of commercial bank retail business in China.
作者 吕巍 陈洁
出处 《系统工程理论方法应用》 北大核心 2005年第6期502-505,共4页 Systems Engineering Theory·Methodology·Applications
基金 上海财经大学现代市场营销研究中心资助项目
关键词 K-MEANS算法 商业银行 零售业务 顾客行为细分 策略 K-means arithmetic commercial bank retail business customer behavior segmentation strategy
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参考文献11

  • 1Lyndon Simkin. Profitable customers: how to identify, develop and retain them [J]. Journal of Targeting, Measurement and Analysis for Marketing, 2002,11(1) :96.
  • 2Mark Pagano. Do you know who your most profitable customers are? [J]. Business Credit,2002,104(5) :26.
  • 3Jan Lindemann. Brand new thinking[J]. Financial World, Canterbury, 2002,48.
  • 4Olivia Parr Rud. CRM special report: Model CRM [J]. Target Marketing, Philadelphia, 2002, 25(6):64.
  • 5Zeithaml Valarie A. The customer pyramid:Creating and serving profitable customers [J].California Management Review, Berkeley, 2001, 43(4):118.
  • 6Patrick Dalton. Profiling profitable customers [J].ABA Bankers News, 2002, (10) : 23.
  • 7Sidney Hill. Mining data for better profits [J].Collections & Credit Risk, 2001,6(4):22.
  • 8Joseph McKendrick. Should they stay or should they go: bank of American works on ways to keep accounts, and to avoid making offers to those it's going to lose anyway[J]. Bank Technology News,2002, 33.
  • 9姜园,张朝阳,仇佩亮,周东方.用于数据挖掘的聚类算法[J].电子与信息学报,2005,27(4):655-662. 被引量:68
  • 10王熙照,王亚东,湛燕,袁方.学习特征权值对K-均值聚类算法的优化[J].计算机研究与发展,2003,40(6):869-873. 被引量:48

二级参考文献60

  • 1刘静,钟伟才,刘芳,焦李成.免疫进化聚类算法[J].电子学报,2001,29(z1):1868-1872. 被引量:43
  • 2刘健庄,谢维信,黄建军,李文化.聚类分析的遗传算法方法[J].电子学报,1995,23(11):81-83. 被引量:27
  • 3钱云涛,谢维信.一种由模糊逻辑神经元网络实现的聚类分析方法[J].西安电子科技大学学报,1995,22(1):1-7. 被引量:12
  • 4钱云涛,谢维信.聚类神经网络的通用设计方法[J].西安电子科技大学学报,1997,24(1):15-21. 被引量:3
  • 5[1]Kurt Thearling. Data Mining,Privacy. A conflict in the Marketing?[M]. California:Edition of Dsstar, 1998.
  • 6[4]Regan Priscilla M,Legislating Privacy. Technology, Social Values, and Public Policy[M].North California:University of North California Press, 1995.
  • 7[6]Jiawei Han, Micheline Kamber.Data Mining: Science and Techniques[M].New York:Morgan Aufmann Publishers, 2000.
  • 8[8][美]迈克尔·A·希特,罗伯特·E·霍斯基森,R·杜安·爱尔兰. 战略管理--竞争与全球化[M].吕巍译. 北京:机械工业出版社, 2002.
  • 9Ester M, Kriegel H P, Sander J, Xu X. A density-based algorithm for discovering clusters in large spatial databases with noise. In Proc. of the 2nd ACM SIGKDD, Portland, 1996:226 - 231.
  • 10Sander J, Ester M, Kriegel H P, Xu X. Denslty-based clustering in spatial databases: the algorithm GDBSCAN and its applications.Data Mining and Knowledge Discovery, 1998, 2(2): 169 - 194.

共引文献128

同被引文献17

  • 1李建平,徐伟宣,石勇.基于主成分线性加权综合评价的信用评分方法及应用[J].系统工程,2004,22(8):64-68. 被引量:14
  • 2周忠宝,董豆豆,周经伦.贝叶斯网络在可靠性分析中的应用[J].系统工程理论与实践,2006,26(6):95-100. 被引量:88
  • 3Duda R O,Hart P E.Pattern classification and science analysis[M].New York:John Wiley & Sons,1973.68-97.
  • 4Mitchell T M.Machine learning[M].New York:McGraw-Hill Companies,Inc,1997.129-184.
  • 5Geiger D,Heckerman D.Knowledge representation and inference in similarity networks and Bayesian Multi-nets[J].Artificial Intelligence,1996,(82):45-74.
  • 6Cheng J,Greiner R.Learning Bayesian belief network classifiers:algorithms and system[A].Proceedings of the Fourteenth Canadian Conference on Artificial Intelligence (AI)[C].Ottawa,2001.101-120.
  • 7Baesens B,Verstraeten G,Van den Poel D.Bayesian network classifiers for identifying the slope of the customer lifecycle of long-life customers[J].European Journal of Operational Research,2004,(156):508-523.
  • 8Friedman N,Geiger D,Goldszmidt M.Bayesian network classifiers[J].Machine Learning,1997,(29):131-163.
  • 9Hand D.Principles of data mining[M].UK:MIT Press,2001.56-92.
  • 10Liautaud B,Hammond M.商务智能[M].北京:电子工业出版社,2002.186-205.

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