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Functional and approximate dependency mining: database and FCA points of view

Abstract : In this paper, we deal with the functional and approximate dependency inference problem by pointing out some relationships between relational database theory and Formal Concept Analysis. More precisely, the notion of functional dependency in database is compared to the notion of implication in Formal Concept Analysis. We propose a framework and several algorithms for mining these dependencies from large database relations. The common data centric step of this framework is the discovery of agree sets, which are closed sets with respect to the closure operator for functional dependency. Two approaches for discovering agree sets from database relations are proposed: the former is a database approach based on SQL queries and the latter is a data mining approach based on partitions. Experiments were performed in order to compare the two proposed methods.
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Contributor : Stéphane Lopes <>
Submitted on : Tuesday, June 10, 2008 - 11:04:41 AM
Last modification on : Monday, April 27, 2020 - 3:44:06 PM


  • HAL Id : hal-00286642, version 1


Stéphane Lopes, Jean-Marc Petit, Lotfi Lakhal. Functional and approximate dependency mining: database and FCA points of view. Journal of Experimental and Theoretical Artificial Intelligence, Taylor & Francis, 2002, 14 (2-3), pp.93-114. ⟨hal-00286642⟩



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