Redundant Association Rules Reduction Techniques

Abstract

Association rule mining has a capability to find hidden correlations among different items within a dataset. To find hidden correlations, it uses two important thresholds known as support and confidence. However, association rule mining algorithms produce many redundant rules though it uses above thresholds. Indeed such redundant rules seem as a main impediment to efficient utilization discovered association rules, and should be removed. To achieve this aim, in the paper, we present several redundant rule elimination methods that first identify the rules that have similar meaning and then eliminate those rules. Furthermore, our methods eliminate redundant rules in such a way that they never drop any higher confidence or interesting rules from the resultant ruleset. The experimental evaluation shows that our proposed methods eliminate a significant number of redundant rules.