A Sensitive Trajectory Clustering Method Based on Spatiotemporal Density

DOI: https://doi.org/10.62517/jike.202304418

Author(s)

Xinyi Wang, Yiwei Liao*, Lei Ji, Zhimin Zhang

Affiliation(s)

School of Computer Science and Information Engineering, Harbin Normal University, Harbin, China *Corresponding Author

Abstract

Trajectory clustering method is a hot research topic in current trajectory data mining. Existing trajectory clustering methods have problems such as inflexible parameter selection, inaccurate clustering results, and inability to quickly obtain reliable clustering results. A sensitive trajectory clustering method based on spatiotemporal density is proposed to address this issue. Firstly, an ordering points to identify the clustering structure algorithm is used to identify the clustering structure to obtain a trajectory clustering decision map, and appropriate clustering parameters are selected. At the same time, considering the spatiotemporal properties of trajectories, a comprehensive analysis and clustering of the spatiotemporal trajectories of each participant were conducted using the Spatial Temporal-density-based Spatial Clustering of Applications with Noise method. The experimental results show that the proposed method can achieve good clustering results while maintaining good time overhead.

Keywords

Clustering Method; Spatiotemporal Trajectory; Trajectory Data Analysis

References

[1] MOU Naixia, Xu Yujing, Zhang Hengcai et al.A review of moving trajectory clustering methods [J]. Bulletin of Surveying and Mapping,2018,(01):1-7.DOI:10.13474/j.cnki.11-2246.2018.0001. [2] LIU Jingyi, Peng Ju, Tang Jianbo, et al.Adaptive clustering method of trajectory data with multi-feature fusion [J]. Journal of Geo-Information Science,2023,25(07):1363-1377. [3] Ji Qiang, Sun Yanfeng, Hu Yongli et al.Review on deep clustering algorithms [J]. Journal of Beijing University of Technology,2021,47(08):912-924. (in Chinese) [4] LIU Jing, Qiu Ziying, Guo Maozu et al.K-means clustering Improvement Algorithm based on Tukey rule and initial center point optimization [J].Data Acquisition and Processing, 2019,38(03):643-651.DOI:10.16337/j.1004-9037.2023.03.013. [5] Xia X N. Random Walk and BIRCH fusion clustering statistical method [J].Statistics and decision, 2021, 5 (23) : 5-9. DOI: 10.13546 / j.carol carroll nki tjyjc. 2021.23.001. [6] Chen Xiaohui, Xi Qinggang.Adaptive clustering algorithm based on DBSCAN is the research and implementation of [J]. Journal of huaiyin normal university (natural science edition), 2021, 20 (3) : 228-234. The DOI: 10.16119 / j.carol carroll nki issn1671-6876.2021.03.007. [7] Huo Zheng, Meng Xiaofeng, Research on trajectory privacy protection technology, Journal of Computer Science, 2011, 34 (10) : 1820-1830. [8] Qin Kun, WANG Yulong, ZHAO Pengxiang et al.Spatiotemporal clustering and analysis of behavior trajectory [J]. Nature Journal,2018,40(03):177-182. (in Chinese) [9]Ankerst M, Breunig M M, Kriegel H P, et al. OPTICS: Ordering points to identify the clustering structure[J]. ACM Sigmod record, 1999, 28(2): 49-60. [10]Birant D, Kut A. ST-DBSCAN: An algorithm for clustering spatial–temporal data[J]. Data & knowledge engineering, 2007, 60(1): 208-221.