Hierarchical trajectory clustering for spatio-temporal periodic pattern mining. (February 2018)

• Record Type: Journal Article
• Authors: Zhang, Dongzhi; Lee, Kyungmi; Lee, Ickjai
• Is Part Of: Expert systems with applications. Volume 92(2018)
• Page Start: 1
• Page End: 11
• Publication Date: 2018-02
• Subjects: Hierarchical trajectory clustering -- Traclus -- Periodic pattern mining -- Reference spots -- Single-linkage; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2017.09.040
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Propose a hierarchical trajectory clustering framework for periodic pattern mining. Propose a trajectory clustering approach that considers additional semantics. Extend the proposed clustering to take into account the sequence of trajectory. Overcome the drawbacks of traditional periodic pattern mining. Provide experimental results to demonstrate the versatility of proposed framework. 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.