Vector Field k-Means: Clustering Trajectories by Fitting Multiple Vector Fields

Ferreira, Nivan; Klosowski, James T.; Scheidegger, Carlos E.; Silva, Cláudio T.

Vector Field k-Means: Clustering Trajectories by Fitting Multiple Vector Fields

Date

2013

Authors

Ferreira, Nivan
Klosowski, James T.
Scheidegger, Carlos E.
Silva, Cláudio T.

Publisher

The Eurographics Association and Blackwell Publishing Ltd.

Abstract

Scientists study trajectory data to understand trends in movement patterns, such as human mobility for traffic analysis and urban planning. In this paper, we introduce a novel trajectory clustering technique whose central idea is to use vector fields to induce a notion of similarity between trajectories, letting the vector fields themselves define and represent each cluster. We present an efficient algorithm to find a locally optimal clustering of trajectories into vector fields, and demonstrate how vector-field k-means can find patterns missed by previous methods. We present experimental evidence of its effectiveness and efficiency using several datasets, including historical hurricane data, GPS tracks of people and vehicles, and anonymous cellular radio handoffs from a large service provider.

        @article{10.1111:cgf.12107
,
journal = {Computer Graphics Forum},
title = {{Vector Field k-Means: Clustering Trajectories by Fitting Multiple Vector Fields
}},
author = {Ferreira, Nivan and 
Klosowski, James T. and 
Scheidegger, Carlos E. and 
Silva, Cláudio T.
},
year = {2013
},
publisher = {The Eurographics Association and Blackwell Publishing Ltd.
},
ISSN = {1467-8659
},
DOI = {10.1111/cgf.12107
}
}

URI

https://doi.org/10.1111/cgf.12107

Collections

32-Issue 3
EuroVis13: Eurographics Conference on Visualization

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