A choice of relevant association rules based on multi-criteria analysis approach

The usefulness and relevance of association rules extracted by the generation algorithms are a critical problem. In fact, in most cases, the real datasets lead to a very large number of association rules, which does not allow users to make their own selection of the most relevant. The searching of the best from the vast array of extracted rules require the identification and use of good measures or techniques of choice. Partial panoramas of these are presented in numerous publications. In this context, we propose a new approach to selecting relevant categories of association rules based on multi criteria analysis using association rules as actions and measures as criteria.