A Novel Algorithm for Associative Classification

Associative classifiers have been the subject of intense research for the last few years. Experiments have shown that they generally result in higher accuracy than decision tree classifiers. In this paper, we introduce a novel algorithm for associative classification " C lassification based on A ssociation R ules G enerated in a B idirectional A pporach" (CARGBA). It generates rules in two steps. At first, it generates a set of high confidence rules of smaller length with support pruning and then augments this set with some high confidence rules of higher length with support below minimum support. Experiments on 6 datasets show that our approach achieves better accuracy than other state-of-the-art associative classification algorithms.