Kavli Affiliate: Jing Wang
| First 5 Authors: Zi Jing Wang, Ye Zhu, Kai Ming Ting, ,
| Summary:
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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