# Distribution-Based Trajectory Clustering

arXiv: https://arxiv.org/abs/2310.05123 · HTML: https://mlsanity.com/paper/2310.05123/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 48 references that are in the map.
- First posted on arXiv 8 Oct 2023.

## Builds on (most cited first, 3 of 3)

- [Kernel Mean Embedding of Distributions: A Review and Beyond](https://mlsanity.com/paper/1605.09522/) (2016)
- [A Survey on Trajectory Data Management, Analytics, and Learning](https://mlsanity.com/paper/2003.11547/) (2020)
- [Riemannian kernel based Nystr"om method for approximate infinite-dimensional covariance descriptors with application to image set classification](https://mlsanity.com/paper/1806.06177/) (2018)

## Built on by (most cited first, 1 of 1)

- [Anomaly Detection Based on Isolation Mechanisms: A Survey](https://mlsanity.com/paper/2403.10802/) (2024)

## Abstract (arXiv)

Trajectory clustering enables the discovery of common patterns in trajectory data. Current methods of trajectory clustering rely on a distance measure between two points in order to measure the dissimilarity between two trajectories. The distance measures employed have two challenges: high computational cost and low fidelity. Independent of the distance measure employed, existing clustering algorithms have another challenge: either effectiveness issues or high time complexity. In this paper, we propose to use a recent Isolation Distributional Kernel (IDK) as the main tool to meet all three challenges. The new IDK-based clustering algorithm, called TIDKC, makes full use of the distributional kernel for trajectory similarity measuring and clustering. TIDKC identifies non-linearly separable clusters with irregular shapes and varied densities in linear time. It does not rely on random initialisation and is robust to outliers. An extensive evaluation on 7 large real-world trajectory datasets confirms that IDK is more effective in capturing complex structures in trajectories than traditional and deep learning-based distance measures. Furthermore, the proposed TIDKC has superior clustering performance and efficiency to existing trajectory clustering algorithms.