Associative classification is a supervised classification method. Many experimental studies have shown that associative classification is a promising approach. There are several associative classification approaches. However, the latter suffer from a major drawback: the huge number of the generated classification rules which takes efforts to select the best ones in order to construct the classifier. To overcome such drawback, we propose in this paper a new direct associative classification method called IGARC, an improvement of GARC approach, that extracts directly generic associative classification rules from a training set in order to reduce the number of associative classification rules without jeopardizing the classification accuracy. A detailed description of this method is presented, as well as the experimentation study on 12 benchmark data sets proving that IGARC is highly competitive in terms of accuracy in comparison with popular classification approaches.