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Recherche efficace de motifs fréquents dans des grilles

Abstract : General-purpose exhaustive graph mining algorithms are seldom used in real life contexts due to the high complexity of the process mostly based on costly isomorphism tests and countless expansion possibilities. In this paper, we show how to exploit grid-based representations to efficiently extract frequent grid subgraphs, and we introduce an efficient grid mining algorithm called GRIMA designed to scale to large amount of data. We apply our algorithm on image classification problems. Experiments show that our algorithm is efficient and that adding the structure may help the image classification process.
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Contributor : Romain Deville Connect in order to contact the contributor
Submitted on : Wednesday, June 1, 2016 - 5:37:44 PM
Last modification on : Tuesday, June 1, 2021 - 2:08:07 PM


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  • HAL Id : hal-01325043, version 1


Romain Deville, Elisa Fromont, Baptiste Jeudy, Christine Solnon. Recherche efficace de motifs fréquents dans des grilles. RFIA 2016, Jun 2016, Clermont Ferrand, France. ⟨hal-01325043⟩



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