计算机应用与软件2017，Vol.34Issue(12)：36-41,6.DOI:10.3969/j.issn.1000-386x.2017.12.007
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
摘要
Abstract
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,but 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Key words
Utility function measurement/Semantic significance/Statistical correlation分类
信息技术与安全科学引用本文复制引用
王仲君,杨文芳..基于效用函数度量的多维效用关联规则挖掘[J].计算机应用与软件,2017,34(12):36-41,6.基金项目
国家自然科学基金面上项目(71671135). （71671135）
