关键词: 粗糙集, 决策表, 聚类率, 属性约简

Abstract: According to classical rough set, when we reduce the attribute of a decision table , it may appear core does not exist that couldn’t find a starting point attribute and be unable to reduction problem. Aiming at this issue, this paper proposes a kind of methodbased on clustering rate of the attribute reduction. This method firstly calculates decision table of the discernibility matrix, attribute to differentiate as the foundation, in the attribute of the same differentiate conditions, the clustering rate fixed attribute importance, guarantee the starting point attribute necessity of existence, so it can find the starting point and win the attributes of the decision table attribute reduction. Experimental results show that the proposed method can make gain the starting pointattribute, and by using the methods of obtaining the reduction results maintains high decision accuracy, and it is effective.

Key words: rough set theory, decision table, clustering rate, attribute reduction