Research on Evaluation Function of Clustering Algorithm Based on Duty Cycle

Density-based clustering (DBSCAN) is one of the most effective methods for trajectory data mining, but density-based clustering algorithms are often limited by the choice of input parameters. In the trajectory data mining, clustering results are not only affected by the within-class distance and between-class distance, but also by the number of coordinate points in the cluster. Therefore, this paper proposes a novel cluster validity index based on the internal and external duty cycle to balance the three factors. In this way, the parameters of density clustering can be automatically selected, and effective clustering can be formed on different datasets. Then the clustering method is applied to the depth analysis and mining of travelers' behavior trajectories. The experiment proves that compared with the traditional validity index, the evaluation function proposed in this paper can optimize input parameters and get better user location information clustering results.