Interestingness Measures for Association Rules in a KDD Process : PostProcessing of Rules with ARQAT Tool

This work takes place in the framework of Knowledge Discovery in Databases (KDD), often called "Data Mining". This domain is both a main research topic and an application ¯eld in companies. KDD aims at discovering previously unknown and useful knowledge in large databases. In the last decade many researches have been published about association rules, which are frequently used in data mining. Association rules, which are implicative tendencies in data, have the advantage to be an unsupervised model. But, in counter part, they often deliver a large number of rules. As a consequence, a postprocessing task is required by the user to help him understand the results. One way to reduce the number of rules - to validate or to select the most interesting ones - is to use interestingness measures adapted to both his/her goals and the dataset studied. Selecting the right interestingness measures is an open problem in KDD. A lot of measures have been proposed to extract the knowledge from large databases and many authors have introduced the interestingness properties for selecting a suitable measure for a given application. Some measures are adequate for some applications but the others are not. In our thesis, we propose to study the set of interestingness measure available in the literature, in order to evaluate their behavior according to the nature of data and the preferences of the user. The ¯nal objective is to guide the user's choice towards the measures best adapted to its needs and in ¯ne to select the most interesting rules. For this purpose, we propose a new approach implemented in a new tool, ARQAT (Association Rule Quality Analysis Tool), in order to facilitate the analysis of the behavior about 40 interest- ingness measures. In addition to elementary statistics, the tool allows a thorough analysis of the correlations between measures using correlation graphs based on the coe±cients suggested by Pear- son, Spearman and Kendall. These graphs are also used to identify the clusters of similar measures. Moreover, we proposed a series of comparative studies on the correlations between interestingness measures on several datasets. We discovered a set of correlations not very sensitive to the nature of the data used, and which we called stable correlations. Finally, 14 graphical and complementary views structured on 5 levels of analysis: ruleset anal- ysis, correlation and clustering analysis, most interesting rules analysis, sensitivity analysis, and comparative analysis are illustrated in order to show the interest of both the exploratory approach and the use of complementary views