An Improvement of Optimal Couverture Extraction Using Semantic Approach

The amount of data speedily proliferates. Consequently, the excessive number of extracted association greatly prohibits to better assist the decision maker. In this respect, backboned on the Formal Concept Analysis, we propose to extend the notion of Formal Concept through the generalization of the notion of itemset aiming to consider the itemset as an intent, its support as the cardinality of the extent. Accordingly, we propose a new approach to extract frequent itemsets using the coverage concept. In fact, this contribution is based on a quality-criterion of a rule namely the profit which expressively improves the classical formal concept analysis through the addition of semantic value to derive highly significant association rules.