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A review of moving object trajectory clustering algorithms

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Abstract

Clustering is an efficient way to group data into different classes on basis of the internal and previously unknown schemes inherent of the data. With the development of the location based positioning devices, more and more moving objects are traced and their trajectories are recorded. Therefore, moving object trajectory clustering undoubtedly becomes the focus of the study in moving object data mining. To provide an overview, we survey and summarize the development and trend of moving object clustering and analyze typical moving object clustering algorithms presented in recent years. In this paper, we firstly summarize the strategies and implement processes of classical moving object clustering algorithms. Secondly, the measures which can determine the similarity/dissimilarity between two trajectories are discussed. Thirdly, the validation criteria are analyzed for evaluating the performance and efficiency of clustering algorithms. Finally, some application scenarios are point out for the potential application in future. It is hope that this research will serve as the steppingstone for those interested in advancing moving object mining.

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Acknowledgments

This work was supported by the natural science foundation of Jiangsu province, China (with Grant of BK20130208), and the Fundamental Research Funds for the Central Universities, China (with Grant of 2013QNA25).

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Authors and Affiliations

  1. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China

    Guan Yuan, Penghui Sun, Jie Zhao & Daxing Li

  2. Jiangsu Key Laboratory of Mine Mechanical and Electrical Equipment, China University of Mining and Technology, No. 1 Daxue Road, Xuzhou, 221116, Jiangsu, China

    Guan Yuan

  3. Department of Information and Engineering, Shandong Management University, Jinan, 250357, China

    Canwei Wang

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  1. Guan Yuan
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Correspondence to Guan Yuan.

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Yuan, G., Sun, P., Zhao, J. et al. A review of moving object trajectory clustering algorithms. Artif Intell Rev 47, 123–144 (2017). https://doi.org/10.1007/s10462-016-9477-7

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