A stochastic approach for association rule extraction

Abstract

This paper addresses the problem of association rule extraction. To extract quantitative association rules from given sets of observations, a stochastic method is proposed. The developed method improves the reliability and interpretability of recognition models based on association rules, employs the stochastic approach to search through various combinations of sets of elements in association rules, and uses a priori information about the informativity of intervals of feature values. A system of criteria for estimating association rules is developed that can be used to automate the analysis of properties and to compare various models based on association rules when solving pattern recognition problems.