Abstract
The popularity of GPS-embedded devices facilitates online monitoring of moving objects and analyzing movement behaviors in a real-time manner. Trajectory clustering acts as one of the most important trajectory analysis tasks, and the researches in this area have been studied extensively in the recent decade. Due to the rapid arrival rate and evolving feature of stream data, little effort has been devoted to online clustering trajectory data streams. In this paper, we propose a framework that consists of two phases, including a micro-clustering phase where a number of micro-clusters represented by compact synopsis data structures are incrementally maintained, and a macro-clustering phase where a small number of macro-clusters are generated based on micro-clusters. Experimental results show that our proposal is both effective and efficient to handle streaming trajectories without compromising the quality.
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Acknowledgement
Our research is supported by the 973 program of China (No. 2012CB316203), NSFC (U1501252, U1401256, 61370101 and 61402180), Shanghai Knowledge Service Platform Project (No. ZF1213), Innovation Program of Shanghai Municipal Education Commission(14ZZ045), and Natural Science Foundation of ShanghaiNo. 14ZR1412600).
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Mao, J., Song, Q., Jin, C., Zhang, Z., Zhou, A. (2016). TSCluWin: Trajectory Stream Clustering over Sliding Window. In: Navathe, S., Wu, W., Shekhar, S., Du, X., Wang, S., Xiong, H. (eds) Database Systems for Advanced Applications. DASFAA 2016. Lecture Notes in Computer Science(), vol 9643. Springer, Cham. https://doi.org/10.1007/978-3-319-32049-6_9
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