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
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 : health risk management Sequential patterns spatiotemporal data mining
Type de document :
Article dans une revue
Intelligent Data Analysis, IOS Press, 2016, 20 (2), pp.293-316. 〈10.3233/IDA-160806〉
Liste complète des métadonnées
https://hal-lirmm.ccsd.cnrs.fr/lirmm-01348460
Contributeur : Isabelle Gouat <>
Soumis le : dimanche 24 juillet 2016 - 06:36:40
Dernière modification le : lundi 22 octobre 2018 - 09:54:03

Identifiants

Collections

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

Citation

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〉

Exporter

BibTeX TEI DC DCterms EndNote

Partager

Métriques

Consultations de la notice

235

Contact

support.ccsd.cnrs.fr
hal.support@ccsd.cnrs.fr
Logo CCSD