CUTiS: optimized online ClUstering of Trajectory data Stream

Recent approaches for online clustering of moving objects location are restricted to instantaneous positions. Subse-quently, they fail to capture the behavior of moving objects over time. By continuously tracking sub-trajectories of moving object at each time window, it becomes possible to gain insight on the current behavior and potentially detect mobility patterns in real time. In our previous work [1], we proposed CUTiS, an incremental algorithm for discovering and maintaining the density-based clusters in trajectory data streams, while tracking the evolution of the clusters. This paper extends [1] to CUTiS* by proposing an indexing structure for sub-trajectory data based on a space-filling curve. The proposed index improves the performance of our approach without losing quality in the clusters results as we show in our experiments conducted on a real dataset.

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