GARCM : Generic Association Rules based Classifier Multi-parameterizable

Many studies in data mining have proposed a new classification approach called associative classification. According to several reports associative classification achieves higher classification accuracy than do traditional classification approaches. However, the associative classification suffers from a major drawback: it is based on the use of a very large number of classification rules; and consequently takes efforts to select the best ones in order to construct the classifier. To overcome such drawback, we propose a new associative classification method called GARCM that exploits a generic basis of association rules in order to reduce the number of association rules without jeopardizing the classification accuracy. Moreover, GARCM proposes to users some interestingness measures that arise from data mining in order to select the best rules during classification of new instances. Carried out experiments on 12 benchmark data sets indicate that GARCM is highly competitive in terms of accuracy in comparison with popular associative classification methods.