An Algorithm for Classification Rules Extraction Based on Discernibility Matrix and Attribute Significance

The attribute reduction and value reduction of rough set were discussed in this paper. The discernibility matrix was extended to value reduction firstly and the attribute significance was redefined based on attribute dependence. An algorithm for classification rules extraction based on discernibility matrix and attribute significance is proposed, which keeps the same classification ability and the minimum attribute reduction to get the effective rules after the value reduction. Compared with the existed algorithm, less time complexity and less space complexity are acquired with this method. Finally, experiment on clothing sales sets verified the effectiveness of the algorithm.