文献类型：期刊文献

英文题名：Customer segmentation for B2C e-commerce websites based on the generalized association rules and decision tree

作者：Ma, Haiying[1]; Gang, Dong[2]

机构：[1] Department of Management Science and Engineering, East China University of Science and Technology, Shanghai, China; [2] College of Computer Science and Technology, Jilin University, Changchun, China

年份：2011

起止页码：4600

外文期刊名：2011 2nd International Conference on Artificial Intelligence, Management Science and Electronic Commerce, AIMSEC 2011 - Proceedings

收录：EI(收录号：20114014387276)

语种：英文

外文关键词：Sales - Decision trees - Managers - Electronic commerce - Websites - Data mining

摘要：Today, as the rapid popularization of Internet applications, many Chinese businesses are attracted by huge profits and market space of e-commerce, beginning to join the area of e-commerce. How to keep effective customervattract more members of the e-commerce website and expand the market effectively, is the problem that all the managers most concerned about. Through studying and comparing common customer segmentation models, this article is proposing a integrated model that combines the techniques of generalized association rules and decision tree. This model is used for customer segmentation for e-commerce websites. It can help managers understand customers, develop markets, and support decision-making. ? 2011 IEEE.