Parallel Approaches for Mining Fuzzy Orderings based Gradual Patterns

Abstract : Mining gradual patterns invokes a number of iterations for generating, adjusting, measuring, and comparing gradual tendencies between numeric attributes of imprecise or uncertain databases. Gradual tendencies are complex correlations of the form {The hight/lower X, the hight/lower Y}. Automatic extraction of such gradual patterns involves huge amounts of processing time, load balance, and high memory consumption. When managing large databases, taking this into account is challenging. In this paper, we show a framework and an algorithm based on rank correlation and fuzzy orderings for mining gradual patterns from imprecise or uncertain data. We also present an approach to improve performance of the algorithm using the parallel programming model of OpenMP and the Yale Sparse Matrix Format to reduce memory consumption. Through an experimental study, we show the performance of our approach with respect to the number of attributes of the databases and the number of cores available.
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Communication dans un congrès
Dietmar Fey, University Erlangen-Nuremberg, Germany, & Jaime Lloret Mauri, Polytechnic University of Valencia, Spain. FUTURE COMPUTING: Future Computational Technologies and Applications, Jul 2012, Nice, France. IARIA, The Fourth International Conference on Future Computational Technologies and Applications. Large-scale Computing Strategies, pp.13-20, 2012, 〈http://www.iaria.org/conferences2012/FUTURECOMPUTING12.html〉
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-00736784
Contributeur : Perfecto Malaquias Quintero Flores <>
Soumis le : samedi 29 septembre 2012 - 19:21:56
Dernière modification le : jeudi 11 janvier 2018 - 06:26:17

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  • HAL Id : lirmm-00736784, version 1

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Perfecto Malaquias Quintero Flores, Del Razo Lopez Federico, Anne Laurent, Sicard Nicolas. Parallel Approaches for Mining Fuzzy Orderings based Gradual Patterns. Dietmar Fey, University Erlangen-Nuremberg, Germany, & Jaime Lloret Mauri, Polytechnic University of Valencia, Spain. FUTURE COMPUTING: Future Computational Technologies and Applications, Jul 2012, Nice, France. IARIA, The Fourth International Conference on Future Computational Technologies and Applications. Large-scale Computing Strategies, pp.13-20, 2012, 〈http://www.iaria.org/conferences2012/FUTURECOMPUTING12.html〉. 〈lirmm-00736784〉

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