Associative classification common research challenges
Institute of Electrical and Electronics Engineers Inc.
Association rule mining involves discovering concealed correlations among variables often from sales transactions to help managers in key business decision involving items shelving, sales and planning. In the last decade, association rule mining methods have been employed in deriving rules from classification dataset in different business domains. This has resulted in an emergence of new classification approach called Associative Classification (AC), which often produces higher predictive classifiers than classic approaches such as decision trees, greedy and rule induction. Nevertheless, AC suffers from noticeable challenges some of which have been inherited from association rules and others have been resulted from building the classifier phase. These challenges are not limited to the massive numbers of candidate ruleitems found, the very large classifiers derived, the inability to handle multi-label datasets, and the design of rule pruning, ranking and prediction procedures. This article highlights and critically analyzes common challenges faced by AC algorithms that are still sustained. Hence, it opens the door for interested researchers to further investigate these challenges hoping to enhance the overall performance of this approach and increase it applicability in research domains. © 2016 IEEE.
This conference paper is not available at CUD collection. The version of scholarly record of this conference paper is published in 2016 45th International Conference on Parallel Processing Workshops (ICPPW) (2016), available online at: https://doi.org/10.1109/ICPPW.2016.67.
Associative classification challenges, Data mining, Prediction, Rules, Association rules, Data mining, Decision trees, Forecasting, Association rule mining methods, Associative classification, Business decisions, Business domain, Classification approach, Research challenges, Research domains, Classification (of information)
Abdelhamid, N., Jabbar, A. A., & Thabtah, F. (2016). Associative classification common research challenges. In Proceedings of the International Conference on Parallel Processing Workshops (Vol. 2016–Septe, pp. 432–437). https://doi.org/10.1109/ICPPW.2016.67