Fuzzy Orderings for Fuzzy Gradual Dependencies: Efficient Storage of Concordance Degrees

Abstract : In this paper, we study the mining of gradual patterns in the presence of numeric attributes belonging to data sets. The field of gradual pattern mining have been recently proposed to extract covariations of attributes, such as: {the higher the age, the higher the salary}. This gradual pattern denoted as {size≥salary≥} means that the age of people increases together with their salary. Actually, the analysis of such correlations is very memory consuming. When managing huge databases, issue is very challenging. In this context, we focus on the use of fuzzy orderings to take this into account and we propose techniques in order to optimize the computation. These techniques are based on a matrix representation of fuzzy concordance degrees C(i, j) and the Yale Sparse Matrix Format.
Type de document :
Communication dans un congrès
WCCI: World Congress on Computational Intelligence, Jun 2012, Brisbane, Australia. Fuzzy Systems (FUZZ-IEEE), 2012 IEEE International Conference on, pp.1-8, 2012, 〈http://www.ieee-wcci2012.org/〉. 〈10.1109/FUZZ-IEEE.2012.6251326〉
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-00736785
Contributeur : Perfecto Malaquias Quintero Flores <>
Soumis le : samedi 29 septembre 2012 - 19:53:08
Dernière modification le : jeudi 11 janvier 2018 - 06:26:17

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Perfecto Malaquias Quintero Flores, Del Razo Lopez Federico, Anne Laurent, Sicard Nicolas, Pascal Poncelet. Fuzzy Orderings for Fuzzy Gradual Dependencies: Efficient Storage of Concordance Degrees. WCCI: World Congress on Computational Intelligence, Jun 2012, Brisbane, Australia. Fuzzy Systems (FUZZ-IEEE), 2012 IEEE International Conference on, pp.1-8, 2012, 〈http://www.ieee-wcci2012.org/〉. 〈10.1109/FUZZ-IEEE.2012.6251326〉. 〈lirmm-00736785〉

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