Hierarchical trajectory clustering for spatio-temporal periodic pattern mining

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.