2011
DOI: 10.1142/s0218195911003652
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Detecting Commuting Patterns by Clustering Subtrajectories

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.

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Cited by 117 publications
(124 citation statements)
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“…One popular form of processing of the gathered data is information summarization, in particular, trajectory clustering; for some recent work see, e.g., [9] and references thereof.…”
Section: Related Workmentioning
confidence: 99%
“…One popular form of processing of the gathered data is information summarization, in particular, trajectory clustering; for some recent work see, e.g., [9] and references thereof.…”
Section: Related Workmentioning
confidence: 99%
“…Second category of online compression techniques perform encoding of points along with eliminating redundant points [8]- [10,20]. In [20] Frchet distance is used to cluster popular subtrajectories in a given trajectory dataset.…”
Section: Related Workmentioning
confidence: 99%
“…Most known approximation algorithms make further assumptions on the curves, and only an O(n 2 )-time approximation algorithm is known for arbitrary polygonal curves [24]. The Fréchet distance and its variants, such as dynamic timewarping [13], have found various applications, with recent work particularly focusing on geographic applications such as map-matching tracking data [15,63] and moving objects analysis [19,20,47].…”
Section: Introductionmentioning
confidence: 99%