Modularity-Based Clustering for Network-Constrained Trajectories

Abstract : We present a novel clustering approach for moving object trajectories that are constrained by an underlying road network. The approach builds a similarity graph based on these trajectories then uses modularity-optimization hiearchical graph clustering to regroup trajectories with similar profiles. Our experimental study shows the superiority of the proposed approach over classic hierarchical clustering and gives a brief insight to visualization of the clustering results.
Type de document :
Communication dans un congrès
20-th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2012), Apr 2012, Bruges, Belgium. pp.471-476, 2012
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https://hal.archives-ouvertes.fr/hal-00695753
Contributeur : Mohamed Khalil El Mahrsi <>
Soumis le : jeudi 4 octobre 2012 - 22:30:12
Dernière modification le : jeudi 9 février 2017 - 15:19:10
Document(s) archivé(s) le : samedi 5 janvier 2013 - 02:50:08

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  • HAL Id : hal-00695753, version 2
  • ARXIV : 1205.2172

Citation

Mohamed Khalil El Mahrsi, Fabrice Rossi. Modularity-Based Clustering for Network-Constrained Trajectories. 20-th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2012), Apr 2012, Bruges, Belgium. pp.471-476, 2012. <hal-00695753v2>

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