ARQAT : an exploratory analysis tool for interestingness measures
Hiep Xuan Huynh, Fabrice Guillet, H. Briand · 2005
Finding interestingness measures to evaluate association rules has become an important knowledge quality issue in KDD. Many interestingness measures may be found in the literature, and many authors have discussed and compared interestingness properties in order to help choose the best measures for a given application. As interestingness depends both on the data structure and on the decision-maker’s goals, some measures may be relevant in some context, but not in others. Therefore, it is necessary to design new contextual approaches in order to help the decision-maker to select the best interestingness measures. In this paper, we present ARQAT a new tool to study the specific behavior of a set of 34 interestingness measures in the context of a specific dataset and in an exploratory data analysis perspective. The tool implements 14 graphical and complementary views structured on 5 levels of analysis: ruleset analysis, correlation and clustering analysis, best rules analysis, sensitivity analysis, and comparative analysis. The tool is described and illustrated on the mushroom dataset in order to show the interest of both the exploratory approach and the use of complementary views.