CMAR: Accurate and efficient classification based on Multiple Class-Association Rules

Previous studies propose that associative classification has high classification accuracy and strong flexibility, at handling unstructured data. However it still suffers from the huge set of mined rules and sometimes biased classification or overfitting since the classification is based on only single high-confidence rule. In this study, we propose a new associative classification method, CMAR, i.e., Classification based on Multiple Association Rules. The method extends an efficient frequent pattern mining method, FP-growth, constructs a class distribution-associated FP-tree, and mines large database efficiently. Moreover, it applies a CR-tree structure to store and retrieve mined association rules efficiently, and prunes rules effectively based on confidence, correlation and database coverage. The classification is performed based on a weighted chi(2) analysis using multiple strong association rules. Our extensive experiments on 26 databases from UCI machine learning database repository, show that CMAR is consistent, highly effective at classification of various kinds of databases and has better average classification accuracy in comparison with CBA and C4.5. Moreover, our performance study, shows that the method is highly efficient and scalable in comparison with other reported associative classification methods.