E<sup>2</sup>DTC: An End to End Deep Trajectory Clustering Framework via Self-Training
Ziquan Fang, Yuntao Du, Chen Lü, Yujia Hu, Yunjun Gao, Gang Chen
Abstract
Trajectory clustering has played an essential role in trajectory mining tasks. It serves in a wide range of real-life applications, including transportation, location-based services, behavioral study, and so on. To support trajectory clustering analytics, a plethora of trajectory clustering methods have been proposed, which mainly extend traditional clustering algorithms by using spatio-temporal characteristics of trajectories. However, existing traditional trajectory clustering approaches based on raw trajectory representation highly rely on hand-craft similarity metrics, and can not capture hidden spatial dependencies in trajectory data, which is inefficient and inflexible for clustering analysis. To this end, we propose an end-to-end deep trajectory clustering framework via self-training, termed as E <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> DTC, inspired by the data-driven capabilities of deep neural networks. E <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> DTC does not require any additional manual feature extraction operations, and can be easily adapted for trajectory clustering analytics on any trajectory dataset. Extensive experimental evaluations on three real-life datasets show that our framework E <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> DTC achieves superior accuracy and efficiency, compared with classical clustering methods (i.e., K-Medoids) and state-of-the-art neural-network based approaches (i.e., t2vec).