Spatio-temporal trajectory region-of-interest mining using Delaunay triangulation

Bermingham, Luke, Lee, Joanne, and Lee, Ickjai (2014) Spatio-temporal trajectory region-of-interest mining using Delaunay triangulation. In: Proccedings of the IEEE International Conference on Data Mining Workshop. pp. 1-8. From: ICDMW 2014: 14th IEEE International Conference on Data Mining Workshops, 14-17 December 2014, Shenzen, China.

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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.

Item ID: 37659
Item Type: Conference Item (Research - E1)
ISBN: 978-1-4799-4275-6
Date Deposited: 05 Mar 2015 01:09
FoR Codes: 08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining @ 100%
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