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Article dans une revue

Spatio-sequential patterns mining: Beyond the boundaries

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.
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-01348460
Déposant : Isabelle Gouat <>
Soumis le : samedi 16 mai 2020 - 10:47:47
Dernière modification le : lundi 30 novembre 2020 - 14:40:02

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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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