Multi-criteria evaluation of interesting dependencies according to a data mining approach

This paper addresses the problem of the goodness of dependency rules extracted by mining data. Our approach is experimental and based on the idea that model quality such as accuracy, interestingness or domain-dependent criteria. Most works on model quality are focusing on one criterion at a time only and do not take into account multiple factors simultaneously. A few works combine different measures in weighted expressions. In order to combine multiple measures, we have first realized a comparative study which highlights the relative contribution of different factors and reveals trade-offs among some of them. This situation suggests looking in the rules which may not exist. Thus, we show that a multi-objective evolutionary approach is able to reveal interesting rules which are ignored by standard solutions.