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.
|Title of host publication||Research and Development in Intelligent Systems XXVII (Proceedings of AI-2010)|
|Editors||Max Bramer, Miltos Petridis, Adrian Hopgood|
|Place of Publication||Cambridge|
|Number of pages||14|
|Publication status||Published - 1 Dec 2010|