Evaluating interestingness measures with linear correlation graph

Abstract

Making comparisons from the post-processing of association rules have become a research challenge in data mining. By evaluating interestingness value calculated from interestingness measures on association rules, a new approach based on the Pearson's correlation coefficient is proposed to answer the question: How we can capture the stable behaviors of interestingness measures on different datasets?. In this paper, a correlation graph is used to evaluate the behavior of 36 interestingness measures on two datasets. © Springer-Verlag Berlin Heidelberg 2006.