Leveraging clustering validation index for detecting 'stops' in spatial trajectory data: a semi-automatic approach.

Spatial trajectory data, interestingly attracting organizations to obtain mobility-based activity patterns of smartphone users. One of the basic objective in this regard is the determination of 'stops' or technically high-density points in the trajectory data. Most works carried out in this area uses variants of density-based clustering algorithms for determining stop points. One of the notable challenges in this area is the determination of the parameters for the clustering algorithm, which highly affects the accuracy of detecting the 'stops'.In this paper a semi-automatic approach is proposed based on particle swarm optimization, DBSCAN, and S_Dbw internal validity index for determining appropriate parameter values for the clustering algorithm and fast convergence. [ABSTRACT FROM AUTHOR]