Principal Association Mining: An efficient classification approach

Abstract

Classification is one of the key tasks in business intelligence, decision science, and machine learning. Associative classification has aroused significant research interest in recent years due to its superior accuracy. Traditional association rule mining algorithms often yield many redundant and sometimes conflicting class association rules. This paper presents a new, efficient associative classification approach. This new approach produces a compact classifier with a small number of association rules, yet with good classification performance. This approach is based on a novel rule quality metric, named as Principality, which measures an association rule’s classification accuracy and coverage for a specific class. Heuristic methods utilizing the Principality metric are applied to rule pruning and associative classifier construction to produce a compact classifier. This Principal Association Mining (PAM) approach is confirmed to be effective at improving classification accuracy as well as decreasing classifier size by experiments conducted on 17 datasets.