期刊文献+

面向E-CRM的数据挖掘技术应用 被引量:6

Application of Data Mining Technology in E-CRM
在线阅读 下载PDF
导出
摘要 简要介绍了CRM在当今商业运作模式中的重要性,并探讨了电子商务时代下CRM向E CRM发展的必然趋势及E CRM的特征。再着重研究了数据挖掘技术在E CRM各个阶段中的应用,其中包括通过构建预测模型来获取新客户、利用决策树技术来挽留老客户、利用聚类技术来进行客户的细分从而使商家不断提高令该客户群满意的能力、及利用预测模型进行交叉营销,向客户提供新的产品和服务等几方面的内容。最后展望了数据挖掘技术的发展趋势和在E CRM中的应用前景。 A brief introduction of the importance of CRM in toda y's business operation mode is given, and the trends of the development of CRM to E-CRM in the e-commerce era and the characteristics of E-CRM are discussed .On the application of data mining technique in various p hases of E-CRM are stressed, including the attraction of new customers by structuring forecasting model, the retainment of old customers with decision tree technology, the classification o f customers with clustering technology so as to increase the enterprise's abilit y to satisfy its customers, the cross marketing with forecasting model, as well as providing new products and new services to customers. In the end, the developing trends of data mining technology and its prospe ctive application in E-CRM are unfolded. 
出处 《控制工程》 CSCD 2003年第3期212-215,共4页 Control Engineering of China
关键词 数据挖掘 E-CRM 商业运作模式 电子商务 决策树 E-commerce CRM data mining
  • 相关文献

参考文献4

二级参考文献7

  • 1[1]Inmon W H. The data warehouse and data mining[J]. Communication of the ACM, 1996,39(11):.
  • 2[2]Fayyad U, Piatetsky-Shapiro G, Smyth P. The KDD process for extracting useful knowledge from volumes of data[J]. Communications of the ACM,1996,39(11):27-35.
  • 3[3]Agrawal R,Mannila H,Srikant Retal. Fast discovery of association rules. Fayyad M,Piatetsky-Shapiro G, Smyth P eds. Advances in Knowledge Discovery and Data Mining[M]. MenloPark, CA:AAAI /MIT Press, 1999.307-328.
  • 4[4]Inmon W H, Hackathorm R. Using the ware house[M]. JohnWiley&Sons,1998.50-150.
  • 5[5]Srikant R,Agrawal R.Mining generalized association rules[A]. Dayal U, Gray PMD, Nishio S eds. Proceedings of the International Conferenceon Very Large Data-bases[C]. San Francisco, CA:Morgan Kanfmann Press,1998.406-419.
  • 6[6]Adriaans P, Zantinge D. Data mining[M]. London:Addison Wesley Longman, 1999.40-100.
  • 7李子木,莫倩,周兴铭.数据仓库技术的研究现状及未来方向[J].计算机科学,1998,25(4):57-59. 被引量:36

共引文献72

同被引文献30

  • 1John Durkin,蔡竞峰,蔡自兴.决策树技术及其当前研究方向[J].控制工程,2005,12(1):15-18. 被引量:64
  • 2黄振华 吴诚一.模式识别[M].杭州:浙江大学出版社,1991.40-62.
  • 3Wilvan der Aalat, Boudewijn Van Dongen, Joachim Herbxt, et al.Workflow mining: A survey of issues and approaches[J]. Data &Knowledge Engineering, 2003, (47): 237 - 267.
  • 4M.Dorigo."Optimization learning and natural algorithms Ph.D.Thesis,Dip.Elettronicae nformazione,Politecnico di Milano,Italy,1992
  • 5Dunham M. Data mining: introductory and advanced topics [M]. Upper Saddle River, NJ: Pearson Education, 2003.
  • 6Friedman N, Geiger D, Tibshirani R. Additive logistic regression: A statistical view of boosting [ J ]. Annals of Stastics, 2000,28 (2) : 337-374.
  • 7Witten I H, Frank E. Data mining practical machine learning tools and techniques( Second Edition ) [ M ]. Beijing: China Machine Press, 2006.
  • 8Landwehr N M, Frank E. Logistic model trees [A]. Proceedings of the Fourteenth European Conference on Machine Learning [ C]. Berlin: Spring- Verlag, 2003.
  • 9Quinlan J R. CA.5: programs for machine learning [M].SanMateo, CA: Morgan Kaufmann, 1993.
  • 10Han J, Kamber M. Data mining: concepts and techniques[M]. San Francisco: Morgan Kaufmann Publishers, 2001.

引证文献6

二级引证文献3

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部