- Clustering vessel trajectories with alignment kernels under trajectory compression
- Lecture Notes in Computer Science
- Pages (from-to)
- Document type
- Faculty of Science (FNWI)
- Informatics Institute (IVI)
In this paper we apply a selection of alignment measures, such as dynamic time warping and edit distance, to the problem of clustering vessel trajectories. Vessel trajectories are an example of moving object trajectories, which have recently become an important research topic. The alignment measures are defined as kernels and are used in the kernel k-means clustering algorithm. We investigate the performance of these alignment kernels in combination with a trajectory compression method. Experiments on a gold standard dataset indicate that compression has a positive effect on clustering performance for a number of alignment measures. Also, soft-max kernels, based on summing all alignments, perform worse than classic kernels, based on taking the score of the best alignment.
- go to publisher's site
- Proceedings title: Machine learning and knowledge discovery in databases: European conference, ECML PKDD 2010, Barcelona,
Spain, September 20-24, 2010: proceedings. - Pt. I
Place of publication: Berlin
Editors: J.L. Balcázar, F. Bonchi, A. Gionis, M. Sebag
If you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library, or send a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible.