Analysis of College Students’ Trajectories Utilizing Data Mining Under Epidemic Prevention and Control

To further strengthen the epidemic prevention and control management in schools, an improved stay point recognition algorithm based on the density-based spatial clustering of applications with noise (DBSCAN) is proposed to achieve accurate recognition of student activity trajectories. The experimental results show that the improved stay point recognition algorithm based on DBSCAN can realize the accurate recognition of student activity trajectories. When the time threshold MinPts is set to 10min and the radius threshold $\varepsilon$ is set to 20m, the recall rate of trajectory stay point recognition reaches 97% and the precision rate reaches 90%. Compared with other algorithms, the recognition algorithm proposed in this paper has a higher recognition accuracy, reaching 0.9873. The above experimental results verify the feasibility of the trajectory analysis method proposed in this paper, which has certain practical value.