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Vers une approche efficace d'extraction de motifs spatio-séquentiels

Abstract : In these last years, large quantity of spatio-temporal data stored leads to new needs such that management of natural risks, health or anthropogenic (e.g. understanding the dynamic of dengue epidemic). In this paper, we define a new theoretical framework for extracting spatio-sequential patterns. A spatio-sequential pattern is a sequence representing evolution of locations and their neighborhoods over time. We propose an efficient algorithm based on depth-first-search with successive projections over the database. We introduce a new interestingness measure taking into account both spatial and temporal aspects. Experiments are conducted on real datasets highlight the relevance of our method.
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-00802118
Contributor : Hugo Alatrista-Salas <>
Submitted on : Friday, October 2, 2020 - 6:35:58 PM
Last modification on : Wednesday, October 14, 2020 - 3:47:03 AM

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  • HAL Id : lirmm-00802118, version 1
  • IRSTEA : PUB00037847

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Hugo Alatrista Salas, Sandra Bringay, Frédéric Flouvat, Nazha Selmaoui-Folcher, Maguelonne Teisseire. Vers une approche efficace d'extraction de motifs spatio-séquentiels. EGC: Extraction et Gestion des Connaissances, Jan 2012, Bordeaux, France. pp.161-172. ⟨lirmm-00802118⟩

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