Identification of stopping points in gps trajectories by two-step clustering based on dpcc with temporal and entropy constraints

HIGHLIGHTS

  • who: Kang Wang and colleagues from the Faculty of Information Technology, Beijing University of Technology, Beijing, China have published the research: Identification of Stopping Points in GPS Trajectories by Two-Step Clustering Based on DPCC with Temporal and Entropy Constraints, in the Journal: Sensors 2022, 23, 3749. of 23/Oct/2008
  • what: To address these challenges, the authors propose a two-step 5 of 17 clustering method using improved density peak clustering for secondary clustering to extract stopping points. For this paper, only the latitude, longitude, and time information will be taken into account. This . . .

     

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