Towards mobility-based clustering
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
An algorithmic framework for segmenting trajectories based on spatio-temporal criteria
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
Processing (multiple) spatio-temporal range queries in multicore settings
ADBIS'11 Proceedings of the 15th international conference on Advances in databases and information systems
Similarity in (spatial, temporal and) spatio-temporal datasets
Proceedings of the 15th International Conference on Extending Database Technology
Warped K-Means: An algorithm to cluster sequentially-distributed data
Information Sciences: an International Journal
Algorithms for hotspot computation on trajectory data
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
| Hi-index | 0.00 |
Trajectory segmentation is the process of partitioning a given trajectory into a small number of homogeneous segments w.r.t. some criteria. Conventional segmentation techniques only focus on the spatial features of the movement and could lead to spatially homogeneous segments but with presumably dissimilar temporal structures. Furthermore, trajectories could be over-segmented in the presence of outliers. In this paper, we propose a family of three trajectory segmentation methods that takes into account both geospatial and temporal structures of movement for the segmentation and is also robust with respect to time-referenced spatial outliers. The effectiveness of our methods is empirically demonstrated over three real-world datasets.