A new tree-based approach for evaluating rule antecedent constraint in confabulation based association rule mining

Abstract

Confabulation-based Association Rule Mining (CARM) is an algorithm that is inspired by the thought process of human brain. It is an iterative method where, in each iteration, new rules are generated using a measure of cogency and constraints on the antecedents and consequents of the previous iteration. This cogency measure leads to a particular ability in dealing with rare items problem. Here, we aim to study CARM's antecedent constraint with respect to the ability of pruning uninteresting rules. Hence, a new Tree-based CARM (TCARM) algorithm is proposed which produces all rules using only cogency constraint on the consequents. In our experiments, the extracted rules of these two algorithms are compared with those of CFPGrowth using several measures such as precision, recall, support and cogency of rules. CFPGrowth is an approach that finds association rules using multiple minimum support and confidence, so the extracted rules of CFPGrowth support rare items and can be used for evaluating the extracted rules of CARM and TCARM. Our analysis based on the proposed TCARM and the extracted rules of CFPGrowth, on both synthetic and real datasets, show that CARM prunes uninteresting rules better than TCARM, which confirms that its antecedent constraint can help to control the number of extracted rules properly.