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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.
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Perfecto Malaquias Quintero Flores, Federico del Razo Lopez, Anne Laurent, Pascal Poncelet, Nicolas Sicard. Fuzzy Orderings for Fuzzy Gradual Dependencies: Efficient Storage of Concordance Degrees. WCCI: World Congress on Computational Intelligence, Jun 2012, Brisbane, Australia. pp.1-8, ⟨10.1109/FUZZ-IEEE.2012.6251326⟩. ⟨lirmm-00736785⟩



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