Subjective evaluation of labeling methods for association rule clustering

Abstract

Among the post-processing association rule approaches, clustering is an interesting one. When an association rule set is clustered, the user is provided with an improved presentation of the mined patters. The domain to be explored is structured aiming to join association rules with similar knowledge. 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 association rule exploration process. Few works have explored and proposed labeling methods for this context. Moreover, these methods have not been explored through subjective evaluations in order to measure their quality; usually, only objective evaluations are used. This paper subjectively evaluates five labeling methods used on association rule clustering. The evaluation aims to find out the methods that presents the best results based on the analysis of the domain experts. The experimental results demonstrate that there is a disagreement between objective and subjective evaluations as reported in other works from literature. © Springer-Verlag 2013.