Directional higher order information for spatio-temporal trajectory dataset

Wang, Ye, Lee, Kyungmi, and Lee, Ickjai (2014) Directional higher order information for spatio-temporal trajectory dataset. In: Proccedings of the IEEE International Conference on Data Mining Workshop, pp. 35-42. From: ICDMW 2014: 14th IEEE International Conference on Data Mining Workshops, 14-17 December 2014, Shenzen, China.

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Abstract

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.

Item ID: 37658
Item Type: Conference Item (Refereed Research Paper - E1)
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ISBN: 978-1-4799-4275-6
Date Deposited: 05 Mar 2015 01:08
FoR Codes: 08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining @ 100%
SEO Codes: 89 INFORMATION AND COMMUNICATION SERVICES > 8902 Computer Software and Services > 890201 Application Software Packages (excl. Computer Games) @ 100%
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