基于关联规则挖掘的粗糙集屙陛值约简算法研究

粗糙集理论中约简是一个重要的研究课题，它包括属性约简和属性值约简两方面内容。针对目前属性值约简只能实现约简，而不能计算各个规则的出现次数的问题，结合关联规则和粗糙集两方面的优点，对冗余规则和不一致规则进行处理，获得具有实际意义的约简表。实验证明，此算法是有效的。

A Study of the Value Reduction Algorithm in Rough Set Based on Association Rules Mining

Reduction is a very important issue in Rough Set, which consists of attribute reduction and value reduction. At present, only reduction can he realised in attribute reduction and the frequency of occurrence for each rule can not he calculated. To resolve the problem, this paper presents a new algorithm which takes the advantages of both association rule and rough set, dealing with redundant rules and inconsistent rules, so that a practical reduction table is obtained. Experiments show that the algorithm is efficient.