The Pattern Next Door: Towards Spatio-sequential Pattern Discovery

Hugo Alatrista Salas 1, 2 Sandra Bringay 3 Frédéric Flouvat 2 Nazha Selmaoui-Folcher 2 Maguelonne Teisseire 1, 3
1 UMR TETIS - Territoires, Environnement, Télédétection et Information Spatiale
2 PPME - EA 3325 - Pôle Pluridisciplinaire de la Matière et de l'Environnement
3 TATOO - Fouille de données environnementales
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : Health risks management such as epidemics study produces large quantity of spatio-temporal data. The development of new methods able to manage such specific characteristics becomes crucial. To tackle this problem, we define a theoretical framework for extracting spatio-temporal patterns (sequences representing evolution of locations and their neighborhoods over time). Classical frequency support doesn't consider the pattern neighbor neither its evolution over time. We thus propose a new interestingness measure taking into account both spatial and temporal aspects. An algorithm based on pattern-growth approach with efficient successive projections over the database is proposed. Experiments conducted on real datasets highlight the relevance of our method.
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Conference papers
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-00802125
Contributor : Hugo Alatrista-Salas <>
Submitted on : Tuesday, March 19, 2013 - 10:38:19 AM
Last modification on : Wednesday, September 18, 2019 - 4:04:04 PM

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  • HAL Id : lirmm-00802125, version 1

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CIRAD | AGROPARISTECH | CNRS | IRSTEA | AGROPOLIS | LIRMM | MIPS | UNIV-MONTPELLIER

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Hugo Alatrista Salas, Sandra Bringay, Frédéric Flouvat, Nazha Selmaoui-Folcher, Maguelonne Teisseire. The Pattern Next Door: Towards Spatio-sequential Pattern Discovery. PAKDD: Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2012, Kuala Lumpur, Malaysia. pp.157-168. ⟨lirmm-00802125⟩

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