Abstract
This paper proposes two novel methods to optimize quantitative association rules. We utilize a multi-objective Genetic Algorithm (GA) in the process. One of the methods deals with partial optimal, and the other method investigates complete optimal. Experimental results on Letter Recognition Database from UCI Machine Learning Repository demonstrate the effectiveness and applicability of the proposed approaches. © Springer-Verlag Berlin Heidelberg 2005.
Cite
CITATION STYLE
Kaya, M., & Alhajj, R. (2005). Novel approach to optimize quantitative association rules by employing multi-objective genetic algorithm. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3533 LNAI, pp. 560–562). Springer Verlag. https://doi.org/10.1007/11504894_78
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