The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-2/W7
https://doi.org/10.5194/isprs-archives-XLII-2-W7-1173-2017
https://doi.org/10.5194/isprs-archives-XLII-2-W7-1173-2017
14 Sep 2017
 | 14 Sep 2017

PARALLEL SPATIOTEMPORAL SPECTRAL CLUSTERING WITH MASSIVE TRAJECTORY DATA

Y. Z. Gu, K. Qin, Y. X. Chen, M. X. Yue, and T. Guo

Keywords: Spectral Clustering, Trajectory data, Spatiotemporal Clustering, DTW, Multi-Thread, Urban Computing

Abstract. Massive trajectory data contains wealth useful information and knowledge. Spectral clustering, which has been shown to be effective in finding clusters, becomes an important clustering approaches in the trajectory data mining. However, the traditional spectral clustering lacks the temporal expansion on the algorithm and limited in its applicability to large-scale problems due to its high computational complexity. This paper presents a parallel spatiotemporal spectral clustering based on multiple acceleration solutions to make the algorithm more effective and efficient, the performance is proved due to the experiment carried out on the massive taxi trajectory dataset in Wuhan city, China.