摘要
传统的规则挖掘算法通常先约简属性再约简属性值.该方法存在冗余计算,当样本集增大时,复杂性急剧增加.对此提出一种基于粒计算的最简决策规则挖掘算法.首先,在不同粒度空间下计算条件粒与决策粒之间的粒关系矩阵;然后,将粒关系矩阵中隐含的信息??1、??2作为启发式算子,按信息粒约简属性值;最后,去除冗余属性并设置终止条件,实现决策规则的快速挖掘.理论分析和实验结果表明,所提出的算法可以获得更简洁的规则,且规则的泛化能力更强.
The traditional rule mining algorithm includes attribute reduction and attribute value reduction, which incorporates redundant computation. The complexity of the algorithm will increase dramatically as the sample dataset increases. Therefore, the granular computing（GrC） method is adopted. Firstly, the granular-relation matrices between condition granules and decision granules in different granular spaces are computed. Then the attribute value is reduced according to H1 and H2 which are hidden in the granular-relation matrices. Furthermore, redundant attributes are removed and the termination condition is set, which can accelerate the mining of decision rules. The theoretical analysis and experimental results show that proposed algorithm can acquire more concise rules, and the rules have better generalizing ability.
出处
《控制与决策》
EI
CSCD
北大核心
2015年第1期143-148,共6页
Control and Decision
基金
国家自然科学基金项目(61402319)
山西省回国留学人员科研项目(2013-031)
关键词
决策规则
粒计算
粒度
粒矩阵
decision rules
granular computing
granularity
granular matrix
