Deep Dirichlet Process Mixture Model for Non-parametric Trajectory Clustering

Di Yao, Jin Wang, Wenjie Chen, Fangda Guo, Peng Han, Jingping Bi

Abstract: Trajectory clustering is an essential task in spatial data mining. To address this problem, many previous studies either extended traditional clustering algorithms with spatial features of trajectories or employed deep learning models for representation learning. However, one common drawback of existing solutions is that the final number of clusters needs to be specified as part of the input. In this paper, we proposed Tra-jDPM, an end-to-end framework for non-parametric trajectory clustering. We come up with two novel loss functions to pretrain a trajectory encoder so as to generate discriminative trajectory representation. Moreover, we employed the neural Dirichlet process mixture model to perform non-parametric clustering based on trajectory embeddings. In this process, the trajectory encoder can also be jointly optimized to improve the performance by a contrastive learning based strategy. We conduct an extensive set of evaluations on several public datasets. Experimental results show that our proposed framework can outperform state-of-the-art methods by a significant margin.