Co-Clustering Network-Constrained Trajectory Data

@inproceedings{Mahrsi2013CoClusteringNT,
  title={Co-Clustering Network-Constrained Trajectory Data},
  author={Mohamed Khalil El Mahrsi and Romain Guigour{\`e}s and Fabrice Rossi and Marc Boull{\'e}},
  booktitle={EGC},
  year={2013}
}
Recently, clustering moving object trajectories kept gaining interest from both the data mining and machine learning communities. This problem, however, was studied mainly and extensively in the setting where moving objects can move freely on the euclidean space. In this paper, we study the problem of clustering trajectories of vehicles whose movement is restricted by the underlying road network. We model relations between these trajectories and road segments as a bipartite graph and we try to… 
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14 11 0 v 1 [ cs . L G ] 2 7 O ct 2 02 1 Mining frequency-based sequential trajectory co-clusters
TLDR
This work proposes a new trajectory co-clustering method that simultaneously clusters the trajectories and their elements taking into account the order in which they appear, and uses the element frequency to identify candidate co- clusters.

References

SHOWING 1-10 OF 30 REFERENCES
Graph-Based Approaches to Clustering Network-Constrained Trajectory Data
TLDR
Two approaches to clustering network-constrained trajectory data are presented and a graph model is used to depict the interactions between observations and clusters of trajectories that traveled along the same parts of the road network are discovered.
Clustering Algorithm for Network Constraint Trajectories
TLDR
A new clustering method for moving object trajectories databases that applies specifically to trajectories that only lie on a predefined network is proposed, inspired from the well-known density based algorithms.
NNCluster: An Efficient Clustering Algorithm for Road Network Trajectories
TLDR
A new distance measure that reflects the spatial proximity of vehicle trajectories on the road network, and an efficient clustering method that reduces the number of distance computations during the clustering process are proposed.
Spatio-temporal Similarity Analysis Between Trajectories on Road Networks
TLDR
Experimental results show that the proposed method to retrieve similar trajectories based on this observation and similarity measure between trajectories on road network space provides not only a practical method for searching for similar trajectoryories but also a clustering method for trajectories.
Continuous Clustering of Moving Objects in Spatial Networks
TLDR
This paper proposes the scheme of clustering continuously moving objects, analyzes the fixed feature of the road network, proposes a notion of Virtual Clustering Unit (VCU) and improves on the existing algorithm.
Modularity-based Clustering for Network-constrained Trajectories
TLDR
A novel clustering approach for moving object trajectories that are constrained by an underlying road network is presented and the superiority of the proposed approach over classic hierarchical clustering is shown.
A graph-based approach to vehicle trajectory analysis
TLDR
Applications with a real data set shows that the proposed graph-based approach can effectively facilitate the understanding of spatial and spatiotemporal patterns in trajectories and discover novel patterns that existing methods cannot find.
On Discovering Moving Clusters in Spatio-temporal Data
TLDR
This work provides a formal definition for moving clusters and describes three algorithms for their automatic discovery, a straight-forward method based on the definition, a more efficient method which avoids redundant checks and an approximate algorithm which trades accuracy for speed by borrowing ideas from the MPEG-2 video encoding.
Clustering moving objects
TLDR
This paper studies the problem of clustering moving objects, which could catch interesting pattern changes during the motion process and provide better insight into the essence of the mobile data points, and proposes efficient techniques to keep the moving micro-clusters geographically small.
Caractérisation de la densité de trafic et de son évolution à partir de trajectoires d'objets mobiles
TLDR
A method for dense route discovery by clustering similar road sections according to both traffic and location in each time period by analyzing spatiotemporal trajectories of moving objects such as vehicles in the road network.
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