基于参考度的关联规则挖掘  

Mining association rules based on consult measure

作　　者：林嘉宜[1] 彭宏[1] 郑启伦[1] 李颖基[1] 

机构地区：[1]华南理工大学计算机科学与工程学院

出　　处：《计算机应用》2005年第8期1827-1829,共3页journal of Computer Applications

基　　金：广东省科技攻关项目(A10202001);广州市科技攻关项目(2004Z2-D0091);广东省自然科学基金资助项目(031454)

摘　　要：针对现有关联规则挖掘的评价标准存在的问题,提出在评价标准中增加参考度,并给出了参考度的定义和基于参考度的关联规则挖掘算法。利用参考度将关联规则分为正关联规则、负关联规则和无效关联规则,从而可以用算法挖掘带有负项的关联规则。最后给出了新算法的实验分析。Some problems of the current measures for association rules were analyzed. A new measure named consult was defined and added to the mining algorithm for association rules. According to the value of consult, association rules were classified into positive, negative and invalid association rules. The new algorithm could find out the negative-item-contained rules. Finally, the algorithm was evaluated and analyzed through experiments and practices.

关 键 词：数据挖掘 关联规则 参考度 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构] TP311.13[自动化与计算机技术—计算机科学与技术]