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RESEARCH ARTICLE   Open Access    

A review of associative classification mining

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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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  • Cite this article

    FADI THABTAH. 2007. A review of associative classification mining. The Knowledge Engineering Review. 22:26 doi: 10.1017/S0269888907001026
    FADI THABTAH. 2007. A review of associative classification mining. The Knowledge Engineering Review. 22:26 doi: 10.1017/S0269888907001026

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RESEARCH ARTICLE   Open Access    

A review of associative classification mining

The Knowledge Engineering Review  22 Article number: 10.1017/S0269888907001026  (2007)  |  Cite this article

Abstract: 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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    FADI THABTAH. 2007. A review of associative classification mining. The Knowledge Engineering Review. 22:26 doi: 10.1017/S0269888907001026
    FADI THABTAH. 2007. A review of associative classification mining. The Knowledge Engineering Review. 22:26 doi: 10.1017/S0269888907001026
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