基于效用函数度量的多维效用关联规则挖掘  

MINING MULTIDIMENSIONAL UTILITY ASSOCIATION RULES BASED ON UTILITY FUNCTION MEASUREMENT

作　　者：王仲君[1] 杨文芳 

机构地区：[1]武汉理工大学理学院,湖北武汉430700

出　　处：《计算机应用与软件》2017年第12期36-41,共6页Computer Applications and Software

基　　金：国家自然科学基金面上项目(71671135)

摘　　要：传统的多维关联规则挖掘过程通常以规则出现的频率来判定规则的有效性,并以支持度与置信度作为度量标准。这种挖掘方法只考虑规则间的统计相关性,忽略了规则自身的语义重要性,即规则能够为商家带来的期望效益。因此在多维关联规则挖掘过程中,引入效用函数作为统计相关性与语义重要性的综合度量指标。效用函数主要从潜在机会、购买概率、期望效益三个方面来度量规则的有效性,潜在机会与购买概率表示统计相关性,期望效益表示语义重要性。结果表明,以效用函数作为度量挖掘出的规则既符合客观上要求的较高频率,又具有主观上期望的较高效益。The traditional multidimensional association rule mining determines the validity of rules by the rule＇s frequency. And it takes support and confidence as measurement standards. This mining method only considers the statistical correlation between rules and ignores the semantic importance which is the effectiveness that the rules can bring. In this paper, we introduce the utility function as a comprehensive measure of statistical correlation and semantic significance. The utility function mainly measures the effectiveness of the rule from three aspects： opportunity, probability and effectiveness. Opportunity and probability represents the statistical correlation, effectiveness represents the semantic significance. The results show that the rules mined by the utility function not only meet higher frequency of objective requirements, hut also have the subjective expectations of higher effectiveness.

关 键 词：效用函数度量 语义重要性 统计相关性 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]