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Characterizing Covers of Functional Dependencies using FCA

Victor Codocedo 1 Jaume Baixeries 2 Mehdi Kaytoue 3 Amedeo Napoli 4 
3 DM2L - Data Mining and Machine Learning
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
4 ORPAILLEUR - Knowledge representation, reasonning
Inria Nancy - Grand Est, LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
Abstract : Functional dependencies (FDs) can be used for various important operations on data, for instance, checking the consistency and the quality of a database (including databases that contain complex data). Consequently, a generic framework that allows mining a sound, complete, non-redundant and yet compact set of FDs is an important tool for many different applications. There are different definitions of such sets of FDs (usually called cover). In this paper, we present the characterization of two different kinds of covers for FDs in terms of pattern structures. The convenience of such a characterization is that it allows an easy implementation of efficient mining algorithms which can later be easily adapted to other kinds of similar dependencies. Finally, we present empirical evidence that the proposed approach can perform better than state-of-the-art FD miner algorithms in large databases.
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Submitted on : Sunday, August 12, 2018 - 7:52:45 AM
Last modification on : Saturday, June 25, 2022 - 7:47:13 PM
Long-term archiving on: : Tuesday, November 13, 2018 - 12:15:40 PM


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  • HAL Id : hal-01856516, version 1


Victor Codocedo, Jaume Baixeries, Mehdi Kaytoue, Amedeo Napoli. Characterizing Covers of Functional Dependencies using FCA. CLA 2018 - The 14th International Conference on Concept Lattices and Their Applications, Jun 2018, Olomouc, Czech Republic. pp.279-290. ⟨hal-01856516⟩



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