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2010
Springer

Evolving Temporal Association Rules with Genetic Algorithms

9 years 1 months ago
Evolving Temporal Association Rules with Genetic Algorithms
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.
Stephen G. Matthews, Mario A. Góngora, Adri
Added 15 Feb 2011
Updated 15 Feb 2011
Type Journal
Year 2010
Where SGAI
Authors Stephen G. Matthews, Mario A. Góngora, Adrian A. Hopgood
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