Co-Clustering Network-Constrained Trajectory Data

Abstract : Recently, clustering moving object trajectories kept gaining interest from both the data mining and machine learning communities. This problem, however, was studied mainly and extensively in the setting where moving objects can move freely on the euclidean space. In this paper, we study the problem of clustering trajectories of vehicles whose movement is restricted by the underlying road network. We model relations between these trajectories and road segments as a bipartite graph and we try to cluster its vertices. We demonstrate our approaches on synthetic data and show how it could be useful in inferring knowledge about the flow dynamics and the behavior of the drivers using the road network.
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Fabrice Guillet; Bruno Pinaud; Gilles Venturini; Djamel Abdelkader Zighed. Advances in Knowledge Discovery and Management, 615, Springer International Publishing, pp.19-32, 2015, Studies in Computational Intelligence, 978-3-319-23750-3. <10.1007/978-3-319-23751-0_2>. <http://link.springer.com/chapter/10.1007/978-3-319-23751-0_2>
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https://hal.archives-ouvertes.fr/hal-01222649
Contributeur : Fabrice Rossi <>
Soumis le : vendredi 30 octobre 2015 - 12:08:09
Dernière modification le : samedi 18 février 2017 - 01:17:31
Document(s) archivé(s) le : vendredi 28 avril 2017 - 05:09:54

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Mohamed Khalil El Mahrsi, Romain Guigourès, Fabrice Rossi, Marc Boullé. Co-Clustering Network-Constrained Trajectory Data. Fabrice Guillet; Bruno Pinaud; Gilles Venturini; Djamel Abdelkader Zighed. Advances in Knowledge Discovery and Management, 615, Springer International Publishing, pp.19-32, 2015, Studies in Computational Intelligence, 978-3-319-23750-3. <10.1007/978-3-319-23751-0_2>. <http://link.springer.com/chapter/10.1007/978-3-319-23751-0_2>. <hal-01222649>

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