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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.
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Contributor : Mohamed Khalil El Mahrsi <>
Submitted on : Thursday, October 4, 2012 - 10:30:12 PM
Last modification on : Friday, July 31, 2020 - 10:44:06 AM
Long-term archiving on: : Saturday, January 5, 2013 - 2:50:08 AM


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


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. ⟨hal-00695753v2⟩



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