Multi objective association rule mining with genetic algorithm without specifying minimum support and minimum confidence

Abstract

Multi objective processing can be leveraged for mining the association rules. This paper discusses the application of multi objective genetic algorithm to association rule mining. We focus our attention especially on association rule mining. This paper proposes a method based on genetic algorithm without taking the minimum support and confidence into account. In order to improve algorithm efficiency, we apply the FP-tree algorithm. Our method extracts the best rules that have best correlation between support and confidence. The operators of our method are flexible for changing the fitness. Unlike the Apriori-based algorithm, it does not depend on support. Experimental study shows that our technique outperforms the traditional methods.