Association Rule Interestingness: Measure and Statistical Validation

Abstract

The search for interesting Boolean association rules is an important topic in knowledge discovery in databases. The set of admissible rules for the selected support and condence thresholds can easily be extracted by algorithms based on support and condence, such as Apriori. However, they may produce a large number of rules, many of them are uninteresting. One has to resolve a two-tier problem: choosing the measures best suited to the problem at hand, then validating the interesting rules against the selected measures. First, the usual measures suggested in the literature will be reviewed and criteria to appreciate the qualities of these measures will be proposed. Statistical validation of the most interesting rules requests performing a large number of tests. Thus, controlling for false discoveries (type I errors) is of prime importance. An original bootstrap-based validation method is proposed which controls, for a given level, the number of false discoveries. The interest of this method for the selection of interesting association rules will be illustrated by several examples.