Discovering Informative Association Rules for Associative Classification

The application of association rule mining to classification has led to a new family of classifiers which are often referred to as associative classifiers (ACs). An advantage of ACs is that they are rule-based and thus lend themselves to an easier interpretation. However, it is common knowledge that association rule mining typically yields a sheer number of rules defeating the purpose of a human readable model. Hence, selecting and ranking a small subset of high-quality rules without jeopardizing the classification accuracy is paramount but very challenging. In this paper, Entropy-AC, a new associative classifier based on entropy, is proposed. Information gain and informative rules are defined at first. Then, the algorithm for constructing associative classifier based on informative rules is presented. Experimental results show the proposed associative classifier is effective.