Title :
Attribute reduction of rough sets in mining market value functions
Author :
Huang, Jiajin ; Liu, Chunnian ; Ou, Chuangxin ; Yao, Y.Y. ; Zhong, Ning
Author_Institution :
Multimedia & Intelligent Software Technol., Beijing Univ. of Technol., China
Abstract :
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.
Keywords :
data mining; marketing; rough set theory; very large databases; ELSA/ANN model; attribute reduction; boosting algorithm; direct marketing; large database; linear model; market value functions; rough set theory; Boosting; Computer science; Data mining; Databases; Entropy; Internet; Laboratories; Predictive models; Rough sets; Set theory;
Conference_Titel :
Web Intelligence, 2003. WI 2003. Proceedings. IEEE/WIC International Conference on
Print_ISBN :
0-7695-1932-6
DOI :
10.1109/WI.2003.1241242
