Spatio-sequential patterns mining: Beyond the boundaries

Hugo Alatrista-Salas 1, 2 Sandra Bringay 3, 4 Frédéric Flouvat 5 Nazha Selmaoui-Folcher 5 Maguelonne Teisseire 2, 4
1 PUCP - Pontificia Universidad Católica del Perú
2 UMR TETIS - Territoires, Environnement, Télédétection et Information Spatiale
3 MIAP - Département de Mathématiques et Informatique Appliquées
4 ADVANSE - ADVanced Analytics for data SciencE
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
5 PPME - EA 3325 - Pôle Pluridisciplinaire de la Matière et de l'Environnement
Abstract : Data mining methods extract knowledge from huge amounts of data. Recently with the explosion of mobile technologies, a new type of data appeared. The resulting databases can be described as spatiotemporal data in which spatial information (e.g., the location of an event) and temporal information (e.g., the date of the event) are included. In this article, we focus on spatiotemporal patterns extraction from this kind of databases. These patterns can be considered as sequences representing changes of events localized in areas and its near surrounding over time. Two algorithms are proposed to tackle this problem: the first one uses \emph{a priori} strategy and the second one is based on pattern-growth approach. We have applied our generic method on two different real datasets related to: 1) pollution of rivers in France; and 2) monitoring of dengue epidemics in New Caledonia. Additionally, experiments on synthetic data have been conducted to measure the performance of the proposed algorithms.
Keywords : spatiotemporal data mining health risk management Sequential patterns
Document type :
Journal articles
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-01348460
Contributor : Isabelle Gouat <>
Submitted on : Sunday, July 24, 2016 - 6:36:40 AM
Last modification on : Wednesday, September 18, 2019 - 4:04:04 PM

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Hugo Alatrista-Salas, Sandra Bringay, Frédéric Flouvat, Nazha Selmaoui-Folcher, Maguelonne Teisseire. Spatio-sequential patterns mining: Beyond the boundaries. Intelligent Data Analysis, IOS Press, 2016, 20 (2), pp.293-316. ⟨10.3233/IDA-160806⟩. ⟨lirmm-01348460⟩

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