V

The interesting association rules is a special part of knowledge extraction from data. Apriori's support- and rule-based algo-rithms have provided an elegant solution to the problem of rule mining, but they produce too much rules, selecting some rules of no interest and ignoring rules[1] [2]. interesting. Other measures are needed to complete the support and the confidance. In this paper, we review the main measures proposed in the literature and we propose criteria to evaluate them. We then suggest a validation method that uses the tools of statistical learning theory, including VC -dimension. Given the large number of measurements and the multitude of candidate rules, the interest of these tools is to allow the construction of uniform non-asymptotic terminals for all the rules and all the measurements simultaneously.