Differential Evolution for Association Rule Mining Using Categorical and Numerical Attributes

Abstract

Association rule mining is a method for identification of dependence rules between features in a transaction database. In the past years, researchers applied the method using features consisting of categorical attributes. Rarely, numerical attributes were used in these studies. In this paper, we present a novel approach for mining association based on differential evolution, where features consist of numerical as well as categorical attributes. Thus, the problem is presented as a single objective optimization problem, where support and confidence of association rules are combined into a fitness function in order to determine the quality of the mined association rules. Initial experiments on sport data show that the proposed solution is promising for future development. Further challenges and problems are also exposed in this paper.