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Spatio-sequential patterns mining: Beyond the boundaries

By Hugo Alatrista-Salas, Sandra Bringay, Frédéric Flouvat, Nazha Selmaoui-Folcher and Maguelonne Teisseire

Abstract

International audienceData 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

Topics: spatiotemporal data mining, health risk management, Sequential patterns, [INFO]Computer Science [cs]
Publisher: 'IOS Press'
Year: 2016
DOI identifier: 10.3233/ida-160806
OAI identifier: oai:HAL:lirmm-01348460v1
Provided by: HAL-CIRAD
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