# Review and Perspective for Distance Based Trajectory Clustering

arXiv: https://arxiv.org/abs/1508.04904 · HTML: https://mlsanity.com/paper/1508.04904/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Aug 2015.

## Built on by (most cited first, 5 of 5)

- [RED: Effective Trajectory Representation Learning with Comprehensive Information](https://mlsanity.com/paper/2411.15096/) (2024)
- [DyCrowd: Towards Dynamic Crowd Reconstruction from a Large-scene Video](https://mlsanity.com/paper/2508.12644/) (2025)
- [An Automated Analysis Framework for Trajectory Datasets](https://mlsanity.com/paper/2202.07438/) (2022)
- [Contrastive Multimodal Learning for Emergence of Graphical Sensory-Motor Communication](https://mlsanity.com/paper/2210.06468/) (2022)
- [Buzz, Choose, Forget: A Meta-Bandit Framework for Bee-Like Decision Making](https://mlsanity.com/paper/2510.16462/) (2025)

## Abstract (arXiv)

In this paper we tackle the issue of clustering trajectories of geolocalized observations. Using clustering technics based on the choice of a distance between the observations, we first provide a comprehensive review of the different distances used in the literature to compare trajectories. Then based on the limitations of these methods, we introduce a new distance : Symmetrized Segment-Path Distance (SSPD). We finally compare this new distance to the others according to their corresponding clustering results obtained using both hierarchical clustering and affinity propagation methods.