Review and Perspective for Distance Based Trajectory Clustering
Machine Learning
2015-08-21 v1 Machine Learning
Applications
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.
Cite
@article{arxiv.1508.04904,
title = {Review and Perspective for Distance Based Trajectory Clustering},
author = {Philippe Besse and Brendan Guillouet and Jean-Michel Loubes and Royer François},
journal= {arXiv preprint arXiv:1508.04904},
year = {2015}
}