Modularity-Based Clustering for Network-Constrained Trajectories

Mohamed Khalil El Mahrsi (1) , Fabrice Rossi (2)

1 LTCI - Laboratoire Traitement et Communication de l'Information
2 SAMM - Statistique, Analyse et Modélisation Multidisciplinaire (SAmos-Marin Mersenne)

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.

Domaines: Machine Learning [stat.ML]; Apprentissage [cs.LG]

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⟩