Towards an Enhanced Semantic Approach Based on Formal Concept Analysis and Lift Measure

The volume of stored data increases rapidly. Therefore, the battery of extracted association heavily prohibits the better support of the decision maker. In this context, backboned on the Formal Concept Analysis, the authors 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, they propose a new approach to extract interesting itemsets through the concept coverage. This approach uses an original quality-criterion of a rule namely the profit improving the classical formal concept analysis through the addition of semantic value in order to extract meaningful association rules.

Provided by: AIRCC Topic: Big Data Date Added: May 2011 Format: PDF

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