DRAC: A Direct Rule Mining Approach for Associative Classification

The application of associative rule mining in classification (associative classification) has demonstrated its power in recent years. The current associative classifier building often adopts three phases: Rule Generation, Building Classifier and Classification. Unfortunately, in rule generation phase, a large number of rules are usually produced, which could not only slow down the mining process but also bring challenge to pruning and storing such magnitude of rules. In this paper, we propose the DRAC, a Direct Rules mining approach for Associative Classification, to tackle the efficiency of associative classification problem. DRAC can mine the high quality non-redundant rule set directly. At the same time, it also adopts the multiple strong class association rules to classify the unlabeled dataset correspondingly. The experimental results show that DRAC is more efficient than traditional approach CBA without losing of accuracy.