Abstract
Clustering trajectory data attracted considerable attention in the last few years. Most of prior work assumed that moving objects can move freely in an euclidean space and did not consider the eventual presence of an underlying road network and its influence on evaluating the similarity between trajectories. In this paper, we present an approach to clustering such network-constrained trajectory data. More precisely we aim at discovering groups of road segments that are often travelled by the same trajectories. To achieve this end, we model the interactions between segments w.r.t. their similarity as a weighted graph to which we apply a community detection algorithm to discover meaningful clusters. We showcase our proposition through experimental results obtained on synthetic datasets.
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El Mahrsi, M.K., Rossi, F. (2013). Graph-Based Approaches to Clustering Network-Constrained Trajectory Data. In: Appice, A., Ceci, M., Loglisci, C., Manco, G., Masciari, E., Ras, Z.W. (eds) New Frontiers in Mining Complex Patterns. NFMCP 2012. Lecture Notes in Computer Science(), vol 7765. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37382-4_9
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DOI: https://doi.org/10.1007/978-3-642-37382-4_9
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