Trajectory clustering
University of Illinois Urbana-Champaign · Korea Advanced Institute of Science and Technology
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…
Citation impact
- FWCI
- 40.67
- Percentile
- 100%
- References
- 27
Authors
3Topics & keywords
- Computer science
- Cluster analysis
- Trajectory
- Artificial intelligence
- Physics