Review & Perspective for Distance Based Trajectory Clustering
Philippe C. Besse, Brendan Guillouet, Jean–Michel Loubes, Royer François · HAL (Le Centre pour la Communication Scientifique Directe) · 2015
In this paper we tackle the issue of clustering trajectories of geolocalized observations. Using clustering technics based on the choice of a distance between the observations, we first provide a comprehensive review of the different distances used in the literature to compare trajectories. Then based on the limitations of these methods, we introduce a new distance : Symmetrized Segment-Path Distance (SSPD). We finally compare this new distance to the others according to their corresponding clustering results obtained using both hierarchical clustering and affinity propagation methods.