The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
Trajectory clustering: a partition-and-group framework
Abstract
Existing trajectory clustering algorithms group similar trajectories as a whole, thus discovering common trajectories. Our key observation is that clustering trajectories as a whole could miss common sub-trajectories. Discovering common sub-trajectories is very useful in many applications, especially if we have regions of special interest for analysis. In this paper, we propose a new partition-and-group framework for clustering trajectories, which partitions a trajectory into a set of line segments, and then, groups similar line segments together into a cluster. The primary advantage of this framework is to discover common sub-trajectories from a trajectory database. Based on this partition-and-group framework, we develop a trajectory clustering algorithm TRACLUS. Our algorithm consists of two phases: partitioning and grouping. For the first phase, we present a formal trajectory partitioning algorithm using the minimum description length(MDL) principle. For the second phase, we present a density-based line-segment clustering algorithm. Experimental results demonstrate that TRACLUS correctly discovers common sub-trajectories from real trajectory data.
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Cites
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Human motion trajectory prediction: a survey
Rudenko, A., Palmieri, L., Herman, M., Kitani, K. M., Gavrila, D. M., & Arras, K. O. (2020) · The International Journal of Robotics Research · doi:10.1177/0278364920917446 · Cited by 658
Yang, D., Wu, L., Wang, S., Jia, H., & Li, K. X. (2019) · Transport Reviews · doi:10.1080/01441647.2019.1649315 · Cited by 461
Discovery of convoys in trajectory databases
Jeung, H., Yiu, M. L., Zhou, X., Jensen, C. S., & Shen, H. T. (2008) · Proceedings of the VLDB Endowment · doi:10.14778/1453856.1453971 · Cited by 431
A clustering-based approach for discovering interesting places in trajectories
Palma, A. T., Bogorny, V., Kuijpers, B., & Alvares, L. O. (2008) · Proceedings of the 2008 ACM symposium on Applied computing · doi:10.1145/1363686.1363886 · Cited by 428
Fernando, T., Denman, S., Sridharan, S., & Fookes, C. (2018) · Neural Networks · doi:10.1016/j.neunet.2018.09.002 · Cited by 371
Metadata
- Authors
- Lee, J.-G., Han, J., & Whang, K.-Y.
- Venue
- Proceedings of the 2007 ACM SIGMOD international conference on Management of data
- Publisher
- ACM
- Published
- 2007-06-11
- Pages
- 593-604
- ISSN
- 0730-8078 (ISSN-L)
- OpenAlex
- W1981398125
- Cited by
- 1,363
- References
- 22
- FWCI
- 41.22
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Lee, J.-G., Han, J., & Whang, K.-Y. (2007). Trajectory clustering: a partition-and-group framework. Proceedings of the 2007 ACM SIGMOD international conference on Management of data. https://doi.org/10.1145/1247480.1247546
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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