摘要： 针对粗糙集分类规则挖掘算法LEM2剪枝条件过于严格的问题，提出一种权重模糊粗糙集的改进规则挖掘算法。在用例带权重的模糊粗糙集理论框架上分析面向混合数据的分类规则挖掘算法，引入粗糙集模型的近似覆盖参数作为挖掘算法的泛化度量参数，实现对规则集数量和规则形式复杂程度的调节。实验结果表明，与LEM2算法和DataSqueezer算法相比，该算法的平均精度和平均召回率更优，分别为81%和80%，且生成规则的平均长度最短。

关键词: 知识发现, 分类, 粗糙集理论, 规则挖掘, 权重学习

Abstract: Aiming at the problem that the pruning condition of the rough set classification rule mining algorithm LEM2 is too strict,this paper proposes an improved rule mining algorithm for weighted fuzzy rough sets.In the framework of fuzzy rough set theory with weights of use cases,the mining method of classification rules for mixed data is discussed.The approximate covering parameters of rough set model are introduced as the generalization metric parameters of mining algorithm,which realizes the adjustment of the number of rule sets and the complexity of the rule form.Experimental results show that compared with LEM2 and DataSqueezer algorithms,the average precision and average recall of the proposed algorithm are better,reaching 81% and 80% respectively,and the average length of the generation rule is the shortest.

Key words: knowledge discovery, classification, rough set theory, rule mining, weighted learning

中图分类号: 

• TP18