基于联合信息熵和粗糙集理论的关联知识发现  

Discovering Knowledge of Association Rules Based on the United Entropy of Information and Rough Sets Theory

作　　者：王天志[1] 吴仕勇[2] 陈恳[1] 王浩[3] 

机构地区：[1]云南师范大学党委宣传部,云南昆明650092 [2]云南师范大学数学学院,云南昆明650092 [3]云南师范大学人事处,云南昆明650092

出　　处：《云南民族大学学报（自然科学版）》2010年第3期224-227,共4页Journal of Yunnan Minzu University:Natural Sciences Edition

基　　金：云南省教育厅科学研究基金(09Y025)

摘　　要：研究了基于联合熵和粗糙集理论的关联规则挖掘算法,改进了基于粗糙集的属性离散化方法—连续属性联合熵差离散化算法;以联合信息熵作为属性约简的标准,提出了基于联合熵的知识约简算法;并给出了以支持度、兴趣度和准确度为阈值的有效关联规则算法.This research focuses on mining association rules based on the united entropy and the Rough Sets Theory.An improved attributes discretization method based on the Rough Sets Theory is proposed,that is,an algorithm of the united entropy difference of successive attributes,and a knowledge reduction algorithm based on united entropy of information is also discussed with the association rules as the standards of accuracy,interestingness and maintenance.

关 键 词：联合熵差 连续属性离散化 知识约简 准确度 关联规则 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]