Abdelhamid, NedaThabtah, Fadi2020-02-132020-02-1320142014Abdelhamid, N., & Thabtah, F. (2014). Associative classification approaches: Review and comparison. Journal of Information and Knowledge Management, 13(3). https://doi.org/10.1142/S021964921450027002196492http://dx.doi.org/10.1142/S0219649214500270https://hdl.handle.net/20.500.12519/140This review is not available at CUD collection. The version of scholarly record of this Review is published in Journal of Information and Knowledge Management (2014), available online at: https://doi.org/10.1142/S0219649214500270.Associative classification (AC) is a promising data mining approach that integrates classification and association rule discovery to build classification models (classifiers). In the last decade, several AC algorithms have been proposed such as Classification based Association (CBA), Classification based on Predicted Association Rule (CPAR), Multi-class Classification using Association Rule (MCAR), Live and Let Live (L3) and others. These algorithms use different procedures for rule learning, rule sorting, rule pruning, classifier building and class allocation for test cases. This paper sheds the light and critically compares common AC algorithms with reference to the abovementioned procedures. Moreover, data representation formats in AC mining are discussed along with potential new research directions. © 2014 World Scientific Publishing Co.enPermission to reuse abstract has been secured from World Scientific Publishing Co. Pte Ltd.Associative classificationclassificationData miningPredictionPruningRule learningRule sortingReviewCopyright : 2014 World Scientific Publishing Co.