Associative classification approaches : review and comparison

dc.contributor.author Abdelhamid, Neda
dc.contributor.author Thabtah, Fadi
dc.date.accessioned 2020-02-13T09:15:16Z
dc.date.available 2020-02-13T09:15:16Z
dc.date.copyright 2014 en_US
dc.date.issued 2014
dc.description This 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. en_US
dc.description.abstract 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. en_US
dc.identifier.citation Abdelhamid, N., & Thabtah, F. (2014). Associative classification approaches: Review and comparison. Journal of Information and Knowledge Management, 13(3). https://doi.org/10.1142/S0219649214500270 en_US
dc.identifier.issn 02196492
dc.identifier.uri http://dx.doi.org/10.1142/S0219649214500270
dc.identifier.uri https://hdl.handle.net/20.500.12519/140
dc.language.iso en en_US
dc.publisher World Scientific Publishing Co. Pte Ltd en_US
dc.relation Authors Affiliations: Abdelhamid, N., Computing and Informatics Department, De Montfort University, Leicester, United Kingdom; Thabtah, F., Ebusiness Department, Canadian University of Dubai, Dubai, United Arab Emirates
dc.relation.ispartofseries Journal of Information and Knowledge Management;Vol. 13, no. 3
dc.rights Permission to reuse abstract has been secured from World Scientific Publishing Co. Pte Ltd.
dc.rights.holder Copyright : 2014 World Scientific Publishing Co.
dc.subject Associative classification en_US
dc.subject classification en_US
dc.subject Data mining en_US
dc.subject Prediction en_US
dc.subject Pruning en_US
dc.subject Rule learning en_US
dc.subject Rule sorting en_US
dc.title Associative classification approaches : review and comparison en_US
dc.type Review en_US
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