Spatio-temporal Trajectory Region-of-Interest Mining Using Delaunay Triangulation

被引：25
|
作者：
Bermingham, Luke [1 ]
Lee, Kyungmi [1 ]
Lee, Ickjai [1 ]
机构：
[1] James Cook Univ, Coll Business Law & Governance, Informat Technol, POB 6811, Cairns, Qld 4870, Australia
来源：
2014 IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOP (ICDMW) | 2014年
关键词：
Trajectory; region of interest; delaunay;
D O I：
10.1109/ICDMW.2014.47
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
引用
收藏
页码：1 / 8
页数：8
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