Online Clustering of Trajectory Data Stream

Abstract

Movement tracking becomes ubiquitous in many applications, which raises great interests in trajectory data analysis and mining. Most existing approaches cluster the whole trajectories offline. This allows characterizing the past movements of the objects but not current patterns. Recent approaches for online clustering of moving objects location are restricted to instantaneous positions. Subsequently, they fail to capture moving objects' behavior over time. By continuously tracking moving objects' sub-trajectories at each time window, rather than just the last position, it becomes possible to gain insight on the current behavior, and potentially detect mobility patterns in real time. In this work, we tackle the problem of discovering and maintaining the density based clusters in trajectory data streams, despite the fact that most moving objects change their position over time. We propose CUTiS, an incremental algorithm to solve this problem, while tracking the evolution of the clusters as well as the membership of the moving objects to the clusters. Our experiments were conducted on real data sets, and it shows the efficiency and the effectiveness of our method.