Application of rough sets in E-commerce consumer behavior prediction

To solve the traditional problem of knowledge acquisition bottleneck in e-commerce, an improved algorithm of attribute reduction based on discernibility matrix is proposed. The algorithm is used to attribute reduction for e-commerce consumer behavior prediction. With rule extraction model of rough sets, the rules of e-commerce consumer behavior prediction are acquired. Practical example of consumer behavior prediction shows that the proposed approach can be handled found knowledge effectively and can be converted the available rules easily. It has strong ability of fault tolerance and can improve the speed and quality of knowledge acquisition. The method has good practical value.