Review and Perspective for Distance Based Trajectory Clustering
Published in
(2015)External Links:
TL
TL;DR
A new distance is introduced : Symmetrized Segment-Path Distance (SSPD) and is compared to the others according to their corresponding clustering results obtained using both hierarchical clustering and affinity propagation methods.
Abstract
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.
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