Multi-criteria decisional approach for extracting relevant association rules

Association rule mining plays a vital role in knowledge discovery in databases. The difficult task is mining useful and non-redundant rules, in fact in most cases, the real datasets lead to a huge number of rules, which does not allow users to make their own selection of the most relevant. Several techniques are proposed such as rule clustering, informative cover method, quality measurements, etc. Another way to selecting relevant association rules, we believe it is necessary to integrate a decisional approach within the knowledge discovery process; to solve the problem, we propose an approach to discover a category of relevant association rules based on multi-criteria analysis (MCA) by using association rules as actions and quality measurements as criteria. Finally, we conclude our work by an empirical study to illustrate the performance of our proposed approach.