  Generating a huge number of association rules reduces their utility in the
decision making process, done by domain experts. In this context, based on the
theory of Formal Concept Analysis, we propose to extend the notion of Formal
Concept through the generalization of the notion of itemset in order to
consider the itemset as an intent, its support as the cardinality of the extent
and its relevance which is related to the confidence of rule. Accordingly, we
propose a new approach to extract interesting itemsets through the concept
coverage. This approach uses a new quality-criteria of a rule: the relevance
bringing a semantic added value to formal concept analysis approach to discover
association rules.
