Evolving Temporal Association Rules with Genetic Algorithms

Conference paper


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.


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This research has been supported by an EPSRC Doctoral Training Account.


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Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  1. 1.Centre for Computational IntelligenceDe Montfort UniversityLeicesterUK

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