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A review of associative classification mining

Thabtah, Fadi Abdeljaber (2007) A review of associative classification mining. Knowledge Engineering Review, 22 (1). pp. 37-65. ISSN 0269-8889

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    Abstract

    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.

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    Item Type: Article
    Additional Information: © 2007, Cambridge University Press
    Uncontrolled Keywords: associative classification mining data association rule discovery technique classifiers CBA CPAR CMAR MCAR MMAC
    Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
    Q Science > QA Mathematics > QA76 Computer software
    Schools: School of Computing and Engineering
    References:

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    Depositing User: Sara Taylor
    Date Deposited: 02 Jul 2007
    Last Modified: 28 Jul 2010 19:20
    URI: http://eprints.hud.ac.uk/id/eprint/269

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