Associative Classification Approaches: Review and Comparison

Neda Abdelhamid () and Fadi Thabtah ()
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Neda Abdelhamid: Computing and Informatics Department, De Montfort University, Leicester, UK
Fadi Thabtah: Ebusiness Department, Canadian University of Dubai, Dubai, UAE

Journal of Information & Knowledge Management (JIKM), 2014, vol. 13, issue 03, 1-30

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.

Keywords: Associative classification; classification; data mining; rule learning; rule sorting; pruning; prediction (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (4)

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DOI: 10.1142/S0219649214500270

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