A two-stage multi-task learning-based method for selective unsupervised domain adaptation

Yong Dai, Jian Zhang, Shuai Yuan, Zenglin Xu

In recent years, clustering trajectory data has been extensively explored to discover similar patterns of moving objects. Existing approaches, often cluster whole life-span trajectories into several groups according to some trajectory similarities such as dynamic time warping and edit distance. However, the trajectory of a given moving object is dynamic and evolved over time. Exploring the dynamic grouping patterns of moving objects (e.g., the expanding, shrinking, emerging or disappearing of clusters) over time thus offers a more dedicated venue to analyze the evolved moving patterns. To address this problem, in this paper, we propose a new any-time trajectory clustering algorithm, called AntClu, building upon the concepts of automatic dynamic trend representation and density-based online clustering. The basic idea is to learn a dynamic representation for each trajectory to capture “current trend”, and then cluster these “trends” in an online setting. Therefore, AntClu is capable of clustering trajectories at any time, and time-changing clusters are available whenever the request comes. More importantly, unlike traditional data stream clustering approaches or online learning, AntClu is also independent of time-window. The experimental results on real-world data sets further demonstrate its effectiveness and efficiency.