Distribution-Based Trajectory Clustering

• URL: http://arxiv.org/abs/2310.05123v2
• Date: Mon, 30 Oct 2023 07:26:44 GMT
• Title: Distribution-Based Trajectory Clustering
• Authors: Zi Jing Wang, Ye Zhu, Kai Ming Ting
• Abstract summary: Trajectory clustering enables the discovery of common patterns in trajectory data. The distance measures employed have two challenges: high computational cost and low fidelity. We propose to use a recent Isolation Distributional Kernel (IDK) as the main tool to meet all three challenges.
• Score: 14.781854651899705
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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