Modularity-Based Clustering for Network-Constrained Trajectories
  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. 
Mohamed Khalil El Mahrsi (LTCI), Fabrice Rossi (SAMM)
stat.ML cs.LG physics.data-an