Discernibility Matrix Enriching and Computation for Attributes Reduction
差别矩阵浓缩及其属性约简求解方法

Attributes reduction is one of important parts researched in rough set theory. Thus, many algorithms have been proposed for attributes reduction, in which the algorithms based on discernibility matrix is one of efficiently attributes reduction algorithms. Unfortunately, these algorithms based on discernibility matrix mainly aim at the consistent decision table, and can not get a correct result for an inconsistent decision table in some cases. Therefore, in this paper, we introduce improved discernibility matrix for computing attributes reduction, which gives an unified framework for a consistent or inconsistent decision table, and efficiently improves the drawback of the existing attributes reduction algorithm based on discerniblity matrix. At the same time, a novel method of improved discernibility matrix enriching is proposed for attributes reduction of a very large dataset.