Labeling Association Rule Clustering through a Genetic Algorithm Approach

Abstract

Among the post-processing association rule approaches, a promising one is clustering. When an association rule set is clustered, the user is provided with an improved presentation of the mined patterns, since he can have a view of the domain to be explored. However, to take advantage of this organization, it is essential that good labels be assigned to the groups, in order to guide the user during the exploration process. Moreover, few works have explored and proposed labeling methods to this context. Therefore, this paper proposes a labeling method, named GLM (Genetic Labeling Method), for association rule clustering. The method is a genetic algorithm approach that aims to balance the values of the measures that are used to evaluate labeling methods in this context. In the experiments, GLM presented a good performance and better results than some other methods already explored.