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A review of associative classification mining

Published online by Cambridge University Press:  01 March 2007

FADI THABTAH
FADI THABTAH
Affiliation:
Department of Computing and Engineering, University of Huddersfield, HD1 3DH, UK; e-mail: f.thabtah@hud.ac.uk
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Abstract

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Associative classification mining is a promising approach in data mining that utilizes the association rule discovery techniques to construct classification systems, also known as associative classifiers. In the last few years, a number of associative classification algorithms have been proposed, i.e. CPAR, CMAR, MCAR, MMAC and others. These algorithms employ several different rule discovery, rule ranking, rule pruning, rule prediction and rule evaluation methods. This paper focuses on surveying and comparing the state-of-the-art associative classification techniques with regards to the above criteria. Finally, future directions in associative classification, such as incremental learning and mining low-quality data sets, are also highlighted in this paper.

Information

Type
Original Article
Information
The Knowledge Engineering Review , Volume 22 , Issue 1 , March 2007 , pp. 37 - 65
Copyright
Copyright © Cambridge University Press 2007

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