Ranking objective interestingness measures with sensitivity values

Abstract

In this paper, we propose a new approach to evaluate the behavior of objective interestingness measures on association rules. The objective interestingness measures are ranked according to the most significant interestingness interval calculated from an inversely cumulative distribution. The sensitivity values are determined by this interval in observing the rules having the highest interestingness values. The results will help the user (a data analyst) to have an insight view on the behaviors of objective interestingness measures and as a final purpose, to select the hidden knowledge in a rule set or a set of rule sets represented in the form of the most interesting rules