Directional higher order information for spatio-temporal trajectory dataset
Conference Publication ResearchOnline@JCUAbstract
Higher order information includes k-nearest neighbor information and k-order region information that are of great importance when the first order or lower order information is not functioning. Despite of the importance of direction in spatio-temporal analysis, directional higher order information has received almost no attention. This paper introduces a new directional higher order information dissimilarity measure that combines topological and geometrical information for spatio-temporal trajectories. It also presents a spider chart-like visualisation approach for directional higher order information and demonstrates the usefulness of this measure with a case study from top-k trajectory mining.
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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
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Publisher Location
Piscataway, NJ, USA
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DOI
10.1109/ICDMW.2014.48