Spatio-temporal trajectory region-of-interest mining using Delaunay triangulation
Conference Publication ResearchOnline@JCUAbstract
Due to the ubiquity of GPS enabled devices and the advances in sensing technologies, trajectory data has become abundant. Regions of interest are important since they describe specific hot-spots within the data that often correlate with domain specific phenomena. Traditional region of interest mining utilises grid based rasters to model space. This suffers from two main problems: hard to determine the best grid size and unable to model consistent spatial adjacency. This paper utilises a 3D argument free space tessellation, Delaunay triangulation, to partition spatio-temporal trajectory data and extract arbitrary shaped regions of interest. Experimental results demonstrate the robustness and improved effectiveness of our approach at identifying granular spatio-temporal patterns.
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Publication Name
ICDMW 2014: 14th IEEE International Conference on Data Mining Workshops
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ISBN/ISSN
978-1-4799-4275-6
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Pages Count
8
Location
Shenzen, China
Publisher
IEEE Computer Society
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Publisher Location
Piscataway, NJ, USA
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DOI
10.1109/ICDMW.2014.47