Integrating classification and association rule mining

TLDR

Classification rule mining seeks a concise set of rules that accurately classify data, whereas association rule mining discovers all frequent rules without a predetermined target. The paper proposes integrating classification and association rule mining. The authors integrate the techniques by mining class association rules and then building a classifier from them using an efficient algorithm. Experiments demonstrate that the CAR‑based classifier outperforms C4.5 and resolves several issues present in existing classification systems.

Abstract

Classification rule mining aims to discover a small set of rules in the database that forms an accurate classifier. Association rule mining finds all the rules existing in the database that satisfy some minimum support and minimum confidence constraints. For association rule mining, the target of discovery is not pre-determined, while for classification rule mining there is one and only one predetermined target. In this paper, we propose to integrate these two mining techniques. The integration is done by focusing on mining a special subset of association rules, called class association rules (CARs). An efficient algorithm is also given for building a classifier based on the set of discovered CARs. Experimental results show that the classifier built this way is, in general, more accurate than that produced by the state-of-the-art classification system C4.5. In addition, this integration helps to solve a number of problems that exist in the current classification systems.