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An Algorithm for Mining Gradual Moving Object Clusters Pattern From Trajectory Streams

Yujie Zhang1, Genlin Ji1,*, Bin Zhao1, Bo Sheng2

School of Computer Science and Technology, Nanjing Normal University, Nanjing, 210023, China.
Department of Computer Science, University of Massachusetts Boston, 100 Morrissey Boulevard, Boston, MA, USA.

* Corresponding Author: Genlin Ji. Email: email.

Computers, Materials & Continua 2019, 59(3), 885-901. https://doi.org/10.32604/cmc.2019.05612

Abstract

The discovery of gradual moving object clusters pattern from trajectory streams allows characterizing movement behavior in real time environment, which leverages new applications and services. Since the trajectory streams is rapidly evolving, continuously created and cannot be stored indefinitely in memory, the existing approaches designed on static trajectory datasets are not suitable for discovering gradual moving object clusters pattern from trajectory streams. This paper proposes a novel algorithm of gradual moving object clusters pattern discovery from trajectory streams using sliding window models. By processing the trajectory data in current window, the mining algorithm can capture the trend and evolution of moving object clusters pattern. Firstly, the density peaks clustering algorithm is exploited to identify clusters of different snapshots. The stable relationship between relatively few moving objects is used to improve the clustering efficiency. Then, by intersecting clusters from different snapshots, the gradual moving object clusters pattern is updated. The relationship of clusters between adjacent snapshots and the gradual property are utilized to accelerate updating process. Finally, experiment results on two real datasets demonstrate that our algorithm is effective and efficient.

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Cite This Article

APA Style
Zhang, Y., Ji, G., Zhao, B., Sheng, B. (2019). An algorithm for mining gradual moving object clusters pattern from trajectory streams. Computers, Materials & Continua, 59(3), 885-901. https://doi.org/10.32604/cmc.2019.05612
Vancouver Style
Zhang Y, Ji G, Zhao B, Sheng B. An algorithm for mining gradual moving object clusters pattern from trajectory streams. Comput Mater Contin. 2019;59(3):885-901 https://doi.org/10.32604/cmc.2019.05612
IEEE Style
Y. Zhang, G. Ji, B. Zhao, and B. Sheng "An Algorithm for Mining Gradual Moving Object Clusters Pattern From Trajectory Streams," Comput. Mater. Contin., vol. 59, no. 3, pp. 885-901. 2019. https://doi.org/10.32604/cmc.2019.05612

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cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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