DETECTING COMMUTING PATTERNS BY CLUSTERING SUBTRAJECTORIES-Reference-Cited by-同舟云学术

DETECTING COMMUTING PATTERNS BY CLUSTERING SUBTRAJECTORIES

Published:2011-06 Issue:03 Volume:21 Page:253-282
ISSN:0218-1959
Container-title:International Journal of Computational Geometry & Applications
language:en
Short-container-title:Int. J. Comput. Geom. Appl.

Author:

BUCHIN KEVIN¹,BUCHIN MAIKE¹,GUDMUNDSSON JOACHIM²,LÖFFLER MAARTEN³,LUO JUN⁴

Affiliation:

1. Department of Mathematics and Computer Science, Technical University Eindhoven, the Netherlands

2. NICTA, Sydney, Australia

3. Computer Science Department University of California, Irvine, USA

4. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China

Abstract

In this paper we consider the problem of detecting commuting patterns in a trajectory. For this we search for similar subtrajectories. To measure spatial similarity we choose the Fréchet distance and the discrete Fréchet distance between subtrajectories, which are invariant under differences in speed. We give several approximation algorithms, and also show that the problem of finding the 'longest' subtrajectory cluster is as hard as MaxClique to compute and approximate.

Publisher

World Scientific Pub Co Pte Ltd

Subject

Applied Mathematics,Computational Mathematics,Computational Theory and Mathematics,Geometry and Topology,Theoretical Computer Science

Link

https://www.worldscientific.com/doi/pdf/10.1142/S0218195911003652

Reference22 articles.

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2. H. Alt and L. Guibas, Handbook of Computational Geometry, eds. J.R. Sack and J. Urrutia (Elsevier, 1999) pp. 121–153.

3. Comparison of Distance Measures for Planar Curves

4. Reporting Leaders and Followers among Trajectories of Moving Point Objects

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