Distribution-Based Trajectory Clustering

TL;DR

This paper introduces TIDKC, a novel trajectory clustering algorithm utilizing the Isolation Distributional Kernel (IDK) to effectively identify complex, irregular clusters in linear time, outperforming existing methods in accuracy and efficiency.

Contribution

It presents a new IDK-based clustering algorithm, TIDKC, that overcomes high computational costs and low fidelity of traditional distance measures, enabling efficient clustering of complex trajectory data.

Findings

TIDKC outperforms existing algorithms in accuracy and efficiency.

IDK captures complex trajectory structures better than traditional measures.

TIDKC is robust to outliers and does not depend on random initialization.

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.