Trajectory clustering method based on spatial-temporal properties for mobile social networks

Abstract

As an important issue in the trajectory mining task, the trajectory clustering technique has attracted lots of the attention in the field of data mining. Trajectory clustering technique identifies the similar trajectories (or trajectory segments) and classifies them into the several clusters which can reveal the potential movement behaviors of nodes. At present, most of the existing trajectory clustering methods focus on some spatial properties of trajectories (such as geographic locations, movement directions), while the spatial-temporal properties (especially the combination of spatial distances and semantic distances) are ignored, and thus some vital information regarding the movement behaviors of nodes is probably lost in the trajectory clustering results. In this paper, we propose a Joint Spatial-Temporal Trajectory Clustering Method (JSTTCM), where some spatial-temporal properties of the trajectories are exploited to cluster the trajectory segments. Finally, the number of clusters and the silhouette coefficient are observed through simulations, and the results show that JSTTCM can cluster the trajectory segments appropriately.

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Acknowledgements

This research is supported by National Natural Science Foundation of China under Grant Nos. 61872191, 61872193, 61972210; Six Talents Peak Project of Jiangsu Province under Grant No. 2019-XYDXX-247.

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Correspondence to Linfeng Liu.

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Tang, J., Liu, L., Wu, J. et al. Trajectory clustering method based on spatial-temporal properties for mobile social networks. J Intell Inf Syst (2020). https://doi.org/10.1007/s10844-020-00607-8

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Keywords

  • Trajectory clustering
  • Spatial-temporal properties
  • Spatial distances
  • Semantic distances