Statistical mining of interesting association rules
This article utilizes stochastic ideas for reasoning about association rule mining, and provides a formal statistical view of this discipline. A simple stochastic model is proposed, based on which support and confidence are reasonable estimates for certain probabilities of the model. Statistical properties of the corresponding estimators, like moments and confidence intervals, are derived, and items and itemsets are observed for correlations. After a brief review of measures of interest of association rules, with the main focus on interestingness measures motivated by statistical principles, two new measures are described. These measures, called alpha- and sigma-precision, respectively, rely on statistical properties of the estimators discussed before. Experimental results demonstrate the effectivity of both measures.
association rules statistical properties interestingness measures precision