Hierarchical trajectory clustering for spatio-temporal periodic pattern mining

Zhang, Dongzhi, Lee, Kyungmi, and Lee, Ickjai (2018) Hierarchical trajectory clustering for spatio-temporal periodic pattern mining. Expert Systems with Applications, 92. pp. 1-11.

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Abstract

Spatio-temporal periodic pattern mining is to find temporal regularities for interesting places. Many real world spatio-temporal phenomena present sequential and hierarchical nature. However, traditional spatio-temporal periodic pattern mining ignores the consideration of sequence, and fails to take into account inherent hierarchy. This paper proposes a hierarchical trajectory clustering based periodic pattern mining that overcomes the two common drawbacks from traditional approaches: hierarchical reference spots and consideration of sequence. We propose a new trajectory clustering algorithm which considers semantic spatio-temporal information such as direction, speed and time based on Traclus and present comparative experimental results with three popular clustering methods: Kernel function, Grid-based, and Traclus. We further extend the proposed trajectory clustering to hierarchical clustering with the use of the single linkage approach to generate a hierarchy of reference spots. Experimental results reveal various hierarchical periodic patterns, and demonstrate that our algorithm outperforms traditional reference spot detection algorithms.

Item ID: 53618
Item Type: Article (Refereed Research - C1)
Keywords: hierarchical trajectory clustering; traclus; periodic pattern mining; reference spots; single-linkage
ISSN: 1873-6793
Date Deposited: 19 Jul 2018 23:57
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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