Evolving temporal association rules with genetic algorithms

Stephen G. Matthews, Mario A. Gongora, Adrian A. Hopgood

Research output: Chapter in Book/Report/Conference proceedingOther chapter contribution

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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.
Original languageEnglish
Title of host publicationResearch and Development in Intelligent Systems XXVII (Proceedings of AI-2010)
EditorsMax Bramer, Miltos Petridis, Adrian Hopgood
Place of PublicationCambridge
PublisherSpringer London
Pages107-120
Number of pages14
ISBN (Electronic)9780857291301
ISBN (Print)9780857291295
DOIs
Publication statusPublished - 1 Dec 2010

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