Attribute reduction of rough sets in mining market value functions

The linear model of market value functions is a new method for direct marketing. Just like other methods in direct marketing, attribute reduction is very important to deal with large databases. We apply the algorithm of attribute reduction, which is based on the combination of rough set theory with the boosting algorithm, to the linear model of market value functions. Experimental results compared with the ELSA/ANN model show that the proposed algorithms can be used effectively in the linear model of market value functions.

[1]  Wang Guo,et al.  Decision Table Reduction based on Conditional Information Entropy , 2002 .

[2]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[3]  Dirk Van den Poel,et al.  Purchase Prediction in Database Marketing with the ProbRough System , 1998, Rough Sets and Current Trends in Computing.

[4]  Yiyu Yao,et al.  Mining market value functions for targeted marketing , 2001, 25th Annual International Computer Software and Applications Conference. COMPSAC 2001.

[5]  Miao Duo-qian,et al.  Information-based algorithm for reduction of knowledge , 1997, 1997 IEEE International Conference on Intelligent Processing Systems (Cat. No.97TH8335).

[6]  Jiming Liu,et al.  Web Intelligence (WI) , 2001, Web Intelligence.

[7]  Filippo Menczer,et al.  An evolutionary multi-objective local selection algorithm for customer targeting , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[8]  Petra Perner,et al.  Data Mining - Concepts and Techniques , 2002, Künstliche Intell..

[9]  Miao Duo,et al.  A HEURISTIC ALGORITHM FOR REDUCTION OF KNOWLEDGE , 1999 .

[10]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[11]  Charles X. Ling,et al.  Data Mining for Direct Marketing: Problems and Solutions , 1998, KDD.

[12]  Ning Zhong,et al.  On Data Mining for Direct Marketing , 2003, RSFDGrC.

[13]  Xiaohua Hu,et al.  A Data Mining Approach for Retailing Bank Customer Attrition Analysis , 2004, Applied Intelligence.

[14]  Janusz Zalewski,et al.  Rough sets: Theoretical aspects of reasoning about data , 1996 .

[15]  Ning Zhong,et al.  Toward Web Intelligence , 2003, AWIC.

[16]  Jiajin Huang,et al.  Using Market Value Functions for Targeted Marketing Data Mining , 2002, Int. J. Pattern Recognit. Artif. Intell..