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

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.