Evolving temporal association rules with genetic algorithms

MATTHEWS, Stephen G., GONGORA, Mario A. and HOPGOOD, Adrian A. (2010). Evolving temporal association rules with genetic algorithms. In: Research and Development in Intelligent Systems. London, Springer, 107-120.

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Official URL: http://dx.doi.org/10.1007/978-0-85729-130-1_8
Link to published version:: https://doi.org/10.1007/978-0-85729-130-1_8
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    Abstract

    A novel framework for mining temporal association rules by discovering itemsets with a genetic algorithm is introduced. Metaheuristics have been applied to association rule mining, we show the efficacy of extending this to another variant - temporal association rule mining. Our framework is an enhancement to existing temporal association rule mining methods as it employs a genetic algorithm to simultaneously search the rule space and temporal space. A methodology for validating the ability of the proposed framework isolates target temporal itemsets in synthetic datasets. The Iterative Rule Learning method successfully discovers these targets in datasets with varying levels of difficulty.

    Item Type: Book Section
    Identification Number: https://doi.org/10.1007/978-0-85729-130-1_8
    Page Range: 107-120
    Depositing User: Adrian Hopgood
    Date Deposited: 30 Aug 2012 15:48
    Last Modified: 13 May 2018 01:23
    URI: http://shura.shu.ac.uk/id/eprint/5652

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