Mining hierarchical semantic periodic patterns from GPS-collected spatio-temporal trajectories

Zhang, Dongzhi, Lee, Kyungmi, and Lee, Ickjai (2019) Mining hierarchical semantic periodic patterns from GPS-collected spatio-temporal trajectories. Expert Systems with Applications, 122. pp. 85-101.

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Abstract

A large number of spatio-temporal trajectory data is being generated from GPS enabled devices such as cars, smartphones, and sensors. These trajectory datasets representing objects' movements provide new opportunities for enhanced spatio-temporal periodic pattern mining. These GPS collected trajectory datasets represent real-world movement phenomena and thus they are spatially placed, temporally recorded, aspatial semantically meaningful, hierarchically structured, and irregularly sampled. Periodic pattern mining from spatio-temporal trajectories is to find temporal regularities from these spatio-temporal trajectories, and thus must take into these five characterisics into account in order not to miss any spatio-temporally, semantically and hierarchically meaningful patterns from irregularly sampled spatio-temporal trajectories. Traditional periodic pattern mining fails to consider these five conditions simultaneously, and in this paper, we propose a hierarchical clustering based semantic periodic pattern mining to consider the five aspects: spatiality, temporality, semantics, hierarchy, and irregularity. Experimental results demonstrate the effectiveness of our proposed method against traditional periodic pattern mining approaches.

Item ID: 57629
Item Type: Article (Research - C1)
ISSN: 1873-6793
Keywords: semantic mining; periodic pattern mining; spatio-temporal trajectory; hierarchy
Copyright Information: Crown Copyright © 2018 Published by Elsevier Ltd. All rights reserved.
Date Deposited: 21 Mar 2019 07:51
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 > 890299 Computer Software and Services not elsewhere classified @ 100%
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