Volume 1 Number 3 (Aug. 2011)
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IJEEEE 2011 Vol.1(3): 269-273 ISSN: 2010-3654
DOI: 10.7763/IJEEEE.2011.V1.44

Construction of Customer Classification Model Based on Inconsistent Decision Table

Li Ju, Xu Wenbin, and Zhou Bei

Abstract—The rough set since proposed has obtained the successful application in each domain. It the oretically has the profound significance and the application. without doubt is one kind of new challenge. This thesis studied the reduction method which can process the in consistent policy-making table directly. This thesis has studied customer classification forecast model which is based on the rough set. Mainly take the rough set theory as the foundation, first gains the data from the CRM system, and convert them into relevant decisiontable. Secondly, we complete and discretize the data in the decisiontable.Thirdly, we reduce the attribute and value. Lastly, we can conclude rules for making decision and establish logic ratiocination system. This thesis validates and analyzes the feasibility of customer classification forecast model. The mining process of customer's knowledge is combined into the system of CRM, constructed intellectual CRM system and realized the automatization between enterprise and customer.

Index Terms—Rough Set, Attribute Reduction, Value Reduction, Rule Reduction, Customer Classification.

Li Ju is with School of Computer Science and Engineering, Chang Shu Institute of Technology, Changshu, Jiangsu, China, phone: 15851528453;e-mail: liju284532@163.com
Xu Wenbin is with School of Computer Science and Engineering, Chang Shu Institute of Technology, Changshu, Jiangsu, China,phone:13962377895, e-mail:50003580@qq.com
Zhou Bei is with School of Computer Science and Engineering, Chang Shu Institute of Technology, Changshu, Jiangsu, China,phone:18915608898 e-mail:327136379@qq.com

Cite: Li Ju, Xu Wenbin, and Zhou Bei, Member, IACSIT, "Construction of Customer Classification Model Based on Inconsistent Decision Table," International Journal of e-Education, e-Business, e-Management and e-Learning vol. 1, no. 3, pp. 269-273, 2011.

General Information

ISSN: 2010-3654
Frequency: Quarterly (Since 2015)
Editor-in-Chief: Prof. Kuan-Chou Chen
Executive Editor: Mr. Ron C. Wu
Abstracting/ Indexing: Google Scholar, Electronic Journals Library, QUALIS, ProQuest, EI (INSPEC, IET)
E-mail: ijeeee@iap.org
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