Customer Behavior Pattern Discovering Based on Mixed Data Clustering

Author

Cheng Mingzhi ; Xin Yang ; Tian Yangge ; Wang Cong ; Yang, Xin

Author_Institution

Inf. Security Center, Beijing Univ. of Posts & Telecommun., Beijing, China

fYear

2009

fDate

11-13 Dec. 2009

Firstpage

1

Lastpage

4

Abstract

To be effective to retain customers and enhance the marketing capabilities, it is necessary to improve the personalization of e-commerce systems. Clustering is a reliable and efficient technology to provide personal service in e-commerce system. However, current research on clustering algorithm usually based on numeric data or categorical data. To analysis customer behavior, mixed data set must be handled. With extending the ROCK algorithm, a novel method to deal with mixed data set was proposed and experiment shows the new algorithm is efficient and successful.

Keywords

consumer behaviour; data analysis; electronic commerce; pattern clustering; ROCK algorithm; customer behavior pattern discovering; e-commerce systems; marketing capabilities; mixed data clustering; Cities and towns; Clustering algorithms; Data analysis; Data mining; Databases; Educational technology; Electronic commerce; Information security; Laboratories; Telecommunication switching;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence and Software Engineering, 2009. CiSE 2009. International Conference on

Conference_Location

Wuhan

Print_ISBN

978-1-4244-4507-3

Electronic_ISBN

978-1-4244-4507-3

Type

conf

DOI

10.1109/CISE.2009.5366556

Filename

5366556