Application of Improved DBSCAN Algorithm in Similarity of Campus Trajectory Data
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    Abstract:

    An improved DBSCAN spatiotemporal clustering algorithm is proposed to increase the intimacy analysis accuracy of social relationships hidden in campus wireless network data. First, spatiotemporal trajectories are formed according to the location and time of the WiFi connection by collecting campus wireless network data, and an improved algorithm is used to classify the spatiotemporal trajectories. Then, the characteristic trajectories of the clustering results are extracted, and the LCSS algorithm is employed to measure the similarity of spatiotemporal trajectories. The high similarity between the trajectories indicates the close relationships, and the low similarity reveals those isolated students that need to be further investigated and counseled by teachers. Finally, FinBI is used to visualize the trajectory clustering results. The experimental results show that the improved algorithm can increase the accuracy and effectiveness of the clustering results while providing a reference for solving other similarity problems.

    Reference
    [1] Nguyen Q, Poquet O, Brooks C, et al. Exploring homophily in demographics and academic performance using spatial-temporal student networks. Proceedings of the 13th International Conference on Educational Data Mining (EDM 2020). International Educational Data Mining Society, 2020. 194–201.
    [2] 王培, 江南, 万幼, 等. 应用Hausdorff距离的时空轨迹相似性度量方法. 计算机辅助设计与图形学学报, 2019, 31(4): 647–658
    [3] 王全民, 赵亚康. 基于PageRank算法的校园好友关系分析. 计算机技术与发展, 2020, 30(1): 140–143. [doi: 10.3969/j.issn.1673-629X.2020.01.025
    [4] 郝美薇, 戴华林, 郝琨. 基于密度的K-means算法在轨迹数据聚类中的优化. 计算机应用, 2017, 37(10): 2946–2951. [doi: 10.11772/j.issn.1001-9081.2017.10.2946
    [5] 王博楠. 基于无线探测的移动用户轨迹分析[硕士学位论文]. 北京: 北京邮电大学, 2017.
    [6] Roberts B, Pahlavan K. Site-specific RSS signature modeling for WiFi localization. 2009 IEEE Global Telecommunications Conference. Honolulu: IEEE, 2009. 1–6.
    [7] 方敏佳, 刘漫丹. 基于校园无线网络的时空轨迹相似性度量. 计算机工程与设计, 2020, 41(11): 3001–3008
    [8] Zhang ZY, Ni GX, Xu YG. Comparison of Trajectory Clustering Methods based on K-means and DBSCAN. IEEE International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA). Chongqing: IEEE, 2020. 557–561.
    [9] 江玉玲, 熊振南, 唐基宏. 基于轨迹段DBSCAN的船舶轨迹聚类算法. 中国航海, 2019, 42(3): 1–5
    [10] Huang ZH, Gao SB, Cai CX, et al. A rapid density method for taxi passengers hot spot recognition and visualization based on DBSCAN+. Scientific Reports, 2021, 11(1): 9420. [doi: 10.1038/s41598-021-88822-3
    [11] 高强, 张凤荔, 王瑞锦, 等. 轨迹大数据: 数据处理关键技术研究综述. 软件学报, 2017, 28(4): 959–992. [doi: 10.13328/j.cnki.jos.005143
    [12] Su H, Zheng K, Huang JM, et al. Calibrating trajectory data for spatio-temporal similarity analysis. The VLDB Journal, 2015, 24(1): 93–116. [doi: 10.1007/s00778-014-0365-y
    [13] Al-Mohy AH, Arslan B. The complex step approximation to the higher order Fréchet derivatives of a matrix function. Numerical Algorithms, 2021, 87(3): 1061–1074. [doi: 10.1007/s11075-020-00998-3
    [14] Andrienko G, Andrienko N, Chen W, et al. Visual analytics of mobility and transportation: State of the art and further research directions. IEEE Transactions on Intelligent Transportation Systems, 2017, 18(8): 2232–2249. [doi: 10.1109/TITS.2017.2683539
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张瑛玺,王法玉.改进DBSCAN算法在校园轨迹数据相似性的应用.计算机系统应用,2022,31(5):364-370

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History
  • Received:August 08,2021
  • Revised:September 13,2021
  • Online: April 11,2022
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