PGLCM: Efficient Parallel Mining of Closed Frequent Gradual Itemsets

Trong Donh Thac Do Anne Laurent 1 Alexandre Termier 2
1 TATOO - Fouille de données environnementales
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
2 LIG Laboratoire d'Informatique de Grenoble - HADAS
LIG - Laboratoire d'Informatique de Grenoble
Abstract : Numerical data (e.g., DNA micro-array data, sensor data) pose a challenging problem to existing frequent pattern mining methods which hardly handle them. In this framework, gradual patterns have been recently proposed to extract covariations of attributes, such as: "When X increases, Y decreases". There exist some algorithms for mining frequent gradual patterns, but they cannot scale to real-world databases. We present in this paper GLCM, the first algorithm for mining closed frequent gradual patterns, which proposes strong complexity guarantees: the mining time is linear with the number of closed frequent gradual itemsets. Our experimental study shows that GLCM is two orders of magnitude faster than the state of the art, with a constant low memory usage. We also present PGLCM, a parallelization of GLCM capable of exploiting multicore processors, with good scale-up properties on complex datasets. These algorithms are the first algorithms capable of mining large real world datasets to discover gradual patterns.
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Communication dans un congrès
ICDM: International Conference on Data Mining, 2001, Sidney, Australia. pp.138-147, 2010
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https://hal.inria.fr/hal-00952985
Contributeur : Fabrice Jouanot <>
Soumis le : vendredi 28 février 2014 - 09:31:15
Dernière modification le : jeudi 24 mai 2018 - 15:59:23

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  • HAL Id : hal-00952985, version 1

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Trong Donh Thac Do, Anne Laurent, Alexandre Termier. PGLCM: Efficient Parallel Mining of Closed Frequent Gradual Itemsets. ICDM: International Conference on Data Mining, 2001, Sidney, Australia. pp.138-147, 2010. 〈hal-00952985〉

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