Trajectory clustering with mixtures of regression models
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Discovering Similar Multidimensional Trajectories
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
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Time-focused clustering of trajectories of moving objects
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Trajectory clustering: a partition-and-group framework
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On the marriage of Lp-norms and edit distance
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Compressing spatio-temporal trajectories
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Finding long and similar parts of trajectories
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Clustering Trajectories of Moving Objects in an Uncertain World
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
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Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
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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.