基于激励的关联规则的挖掘  

Motivation-based association rule mining

作　　者：刘旭辉[1] 邵世煌[2] 余光柱[2,3] 

机构地区：[1]长江大学机械工程学院,湖北荆州434000 [2]东华大学信息科学与技术学院,上海201600 [3]湖北警官学院计算机系,武汉430034

出　　处：《计算机应用》2009年第1期189-192,共4页journal of Computer Applications

基　　金：国家教育部博士点基金资助项目(20060255006)

摘　　要：基于支持度的关联规则挖掘算法无法找到那些非频繁但效用很高的项集,基于效用的关联规则会漏掉那些效用不高但发生比较频繁、支持度和效用值的积(激励)很大的项集。提出了基于激励的关联规则挖掘问题及一种自下而上的挖掘算法HM-miner。激励综合了支持度与效用的优点,能同时度量项集的统计重要性和语义重要性。HM-miner利用激励的上界特性进行减枝,能有效挖掘高激励项集。The existing algorithms for support-based Association Rule Mining （ARM） cannot find the itemsets that are not frequent but have high utility values, while Utility-Based Association Rule Mining （UBARM） cannot find the itemsets whose utility values are not high but the product of the support and utility of the same itemset （ defined as motivation） is very large. This paper proposed motivation-based association rule and a down-top algorithm called HM-miner to find all high motivation itemsets efficiently. By integrating the advantages of support and utility, the new measure, i.e., motivation can measure both the statistical and semantic significance of an itemset. HM-miner adopted a new pruning strategy, which was based on the motivation upper bound property, to cut down the search space.

关 键 词：关联规则 基于激励 支持度 效用 兴趣度 

分 类 号：TP182[自动化与计算机技术—控制理论与控制工程]