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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/9492

Title: A clustering-based approach for discovering interesting places in trajectories
Authors: Palma, Andrey Tietbohl
ALVARES, Luis Otavio
Issue Date: 2008
Publisher: ACM
Citation: Wainwright, Roger & Haddad, Hisham (Ed.) Proceedings of the 2008 ACM Symposium on Applied Computing (SAC). p. 863-868.
Series/Report: Symposium on Applied Computing
Abstract: Because of the large amount of trajectory data produced by mobile devices, there is an increasing need for mechanisms to extract knowledge from this data. Most existing works have focused on the geometric properties of trajectories, but recently emerged the concept of semantic trajectories, in which the background geographic information is integrated to trajectory sample points. In this new concept, trajectories are observed as a set of stops and moves, where stops are the most important parts of the trajectory. Stops and moves have been computed by testing the intersections of trajectories with a set of geographic objects given by the user. In this paper we present an alternative solution with the capability of finding interesting places that are not expected by the user. The proposed solution is a spatio-temporal clustering method, based on speed, to work with single trajectories. We compare the two different approaches with experiments on real data and show that the computation of stops using the concept of speed can be interesting for several applications.
URI: http://hdl.handle.net/1942/9492
Link to publication: http://doi.acm.org/10.1145/1363686.1363886
ISBN: 978-1-59593-753-7
Category: C1
Type: Proceedings Paper
Appears in Collections: Research publications

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