PGP-mc: Towards a Multicore Parallel Approach for Mining Gradual Patterns

Anne Laurent 1 Benjamin Negrevergne 2 Nicolas Sicard 3 Alexandre Termier 4
1 TATOO - Fouille de données environnementales
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
2 MESCAL - Middleware efficiently scalable
Inria Grenoble - Rhône-Alpes, LIG - Laboratoire d'Informatique de Grenoble
3 EFREI - École d'ingénieur généraliste en informatique et technologies du numérique
4 LIG Laboratoire d'Informatique de Grenoble - HADAS
LIG - Laboratoire d'Informatique de Grenoble
Abstract : Gradual patterns highlight complex order correlations of the form "The more/less X, the more/less Y". Only recently algorithms have appeared to mine efficiently gradual rules. However, due to the complexity of mining gradual rules, these algorithms cannot yet scale on huge real world datasets. In this paper, we propose to exploit parallelism in order to enhance the performances of the fastest existing one (GRITE). Through a detailed experimental study, we show that our parallel algorithm scales very well with the number of cores available.
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Anne Laurent, Benjamin Negrevergne, Nicolas Sicard, Alexandre Termier. PGP-mc: Towards a Multicore Parallel Approach for Mining Gradual Patterns. DASFAA: Database Systems for Advanced Applications, Apr 2010, Tsukuba, Japan. pp.78-84, ⟨10.1007/978-3-642-12026-8_8⟩. ⟨hal-00788890⟩

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