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.
This research was initiated during the GADGET Workshop on Geometric Algorithms and Spatial Data Mining, funded by the Netherlands Organisation for Scientific Research (NWO) under BRICKS/FOCUS grant number 642.065.503. The research was further supported by NWO through the GADGET and GOGO projects, and by the Australian Government’s Backing Australia’s Ability initiative, in part through the Australian Research Council.
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Buchin, K., Buchin, M., Gudmundsson, J., Löffler, M., Luo, J. (2008). Detecting Commuting Patterns by Clustering Subtrajectories . In: Hong, SH., Nagamochi, H., Fukunaga, T. (eds) Algorithms and Computation. ISAAC 2008. Lecture Notes in Computer Science, vol 5369. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92182-0_57
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DOI: https://doi.org/10.1007/978-3-540-92182-0_57
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