Analysis of Various Interestingness Measures in Class Association Rule Mining

Abstract

Many measures have been developed to determine the interestingness of rules in data mining. Numerous studies have shown that the effects of different measures depend on the concrete problems, and different measures usually provide different and conflicting results. Therefore, selecting the appropriate measure becomes an important issue in data mining. In this paper, a novel approach to select the appropriate measure for class association rule mining is proposed. The proposed approach is applied to several problems, including benchmark and real-world datasets. The experimental results show that the proposed approach is a powerful tool to analyze various measures to select the right ones for the concrete problems, leading to the increase of the classification accuracy. Based on the study, this paper further proposes four properties of interestingness measures that should be considered in class association rule mining.