Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications
Data Mining and Knowledge Discovery
Trajectory clustering: a partition-and-group framework
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Mining user similarity based on location history
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
Semantic trajectory mining for location prediction
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
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To study the similarity between moving object trajectories is important in many applications, e.g., to find the clusters of moving objects which share the same moving pattern, and infer the future locations of a moving object from its similar trajectories. To define the similarity between moving objects is a challenging task, since not only their locations change but also their speed and semantic features vary. In this paper, we propose a novel approach to measure the similarity between trajectories. The similarity is defined based on both geographic and semantic features of movements. Our approach can be used to detect trajectory clusters and infer future locations of moving objects.