Novel approach to optimize quantitative association rules by employing multi-objective genetic algorithm

Authors:
Mehmet Kaya;Reda Alhajj
Affiliations:
Dept of CENG, Firat University, Elazig, Turkey Dept of CS, University of Calgary, Calgary, AB, Canada;Dept of CENG, Firat University, Elazig, Turkey Dept of CS, University of Calgary, Calgary, AB, Canada
Venue:
IEA/AIE'2005 Proceedings of the 18th international conference on Innovations in Applied Artificial Intelligence
Year:
2005

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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.