关键词: 知识粒度, 属性相似度, 属性约简

Abstract: Attribute reduction is very important to knowledge acquisition in rough set theory. According to the consistency of attribute similarity and knowledge granularity, a new algorithm for attribute reduction based on knowledge granularity is proposed by calculating the similarity between condition attributes and decision attributes, as well as the similarity between condition attributes. The condition attributes are ordered descendingly based on the similarity between condition attributes and decision attributes, and the reduction set is obtained by selecting the important attributes based on the similarity between condition attributes. Theory analysis and the experiment results show that this algorithm reduces the calculation complexity and improves reduction effect.

Key words: knowledge granularity, attribute similarity, attribute reduction