A Heuristic Algorithm of Attribute Reduction for Decision Table Based on Discernibility Matrix

Attribute reduction is one of the cardinal contents of research for theory of rough sets, and is a key step of knowledge acquisition. According to the attribute that the number of attributes is less in a discernibility matrix element, a fast search attribute reduction algorithm is proposed by using the importance of attribute based on discernibility matrix of decision table and attribute's frequency in the union set of matrix elements. It avoids the unfeasibility of attribute reduction based on discernibility matrix in larger database and the flaw of attribute reduction algorithm based on attribute frequency of discernibility matrix because of the number of attributes unconsidered in discernibility matrix elements. It has proved to be effective by the result of experiment.