吉林大学学报(工学版) ›› 2015, Vol. 45 ›› Issue (3): 913-920.doi: 10.13229/j.cnki.jdxbgxb201503033

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Discovering spatiotemporal hot spot region and mining patterns fro moving trajectory random sampling

WANG Liang1, 2, 3, HU Kun-yuan1, KU Tao1, WU Jun-wei1, 2   

  1. 1.Shenyang Institute of Automation,Chinese Academy of Sciences, Shenyang 110016,China;
    2.University of Chinese Academy of Sciences, Beijing 100039,China;
    3.School of Electrical and Control Engineering,Xi'an University of Science and Technology,Xi'an University of Science and Technology,Xi'an 710054,China
  • Received:2013-08-30 Online:2015-05-01 Published:2015-05-01

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Abstract: The moving trajectory by random sampling distributes unevenly in time dimension. After projecting the three-dimensional spatiotemporal trajectory data into one-dimensional time domain, a spatiotemporal hot spot region discovery and moving pattern mining methods are proposed based on automatic detection of intensive time intervals. Through detecting intensive time intervals dynamically with a bottom-up clustering strategy, the spatiotemporal hot spot regions are discovered in corresponding time intervals. A depth-first algorithm is designed to mine the set of frequency moving patterns. Finally, based on synthetic moving trajectory dataset, the effectiveness and scalability of the proposed algorithms are verified.

Key words: artificial intelligence, data mining, moving trajectory by random sampling, intensive time interval, hot spot region

CLC Number: 

  • TP18
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