Associative classification common research challenges

dc.contributor.authorAbdelhamid, Neda
dc.contributor.authorJabbar, Ahmad Abdul
dc.contributor.authorThabtah, Fadi
dc.descriptionThis 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:
dc.description.abstractAssociation 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.en_US
dc.identifier.citationAbdelhamid, 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).
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relationAuthors Affiliations: Abdelhamid, N., Information Technology, Auckland Institute of Studies, Auckland, New Zealand; Jabbar, A.A., Ebusiness Department, Canadian University of Dubai, Dubai, United Arab Emirates; Thabtah, F., Applied Business, Nelson Marlborough Institute of Technology, Auckland, New Zealand
dc.relation.ispartofseriesProceedings of the International Conference on Parallel Processing Workshops;2016-September
dc.rightsPermission to reuse abstract has been secured from Institute of Electrical and Electronics Engineers Inc.
dc.rights.holderCopyright : 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.subjectAssociative classification challengesen_US
dc.subjectData miningen_US
dc.subjectAssociation rulesen_US
dc.subjectData miningen_US
dc.subjectDecision treesen_US
dc.subjectAssociation rule mining methodsen_US
dc.subjectAssociative classificationen_US
dc.subjectBusiness decisionsen_US
dc.subjectBusiness domainen_US
dc.subjectClassification approachen_US
dc.subjectResearch challengesen_US
dc.subjectResearch domainsen_US
dc.subjectClassification (of information)en_US
dc.titleAssociative classification common research challengesen_US
dc.typeConference Paperen_US
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