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.
@article{arxiv.1205.2172,
title = {Modularity-Based Clustering for Network-Constrained Trajectories},
author = {Mohamed Khalil El Mahrsi and Fabrice Rossi},
journal= {arXiv preprint arXiv:1205.2172},
year = {2012}
}
Comments
20-th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2012), Bruges : Belgium (2012)