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Modularity-Based Clustering for Network-Constrained Trajectories (1205.2172v2)
Published 10 May 2012 in stat.ML, cs.LG, and physics.data-an
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.