Interestingness of Association Rules Using Symmetrical Tau and Logistic Regression

摘要: While association rule mining is one of the most popular data techniques, it usually results in many rules, some which are not considered as interesting or significant for application at hand. In this paper, we conduct a systematic approach to ascertain discovered rules and provide rigorous statistical supporting framework. The strategy proposed combines measurement including redundancy analysis, sampling multivariate discard non rules. A real world dataset used demonstrate how unified framework can redundant still preserve high accuracy set whole.