Spatiotemporal trajectory clustering: A clustering algorithm for spatiotemporal data

Spatial technologies generate large datasets quickly and continuously. The purpose of this study is to develop a clustering algorithm to mine spatiotemporal co-location events in trajectory datasets. We present a spatiotemporal algorithm for sub-trajectory clustering that divides a trajectory into line segments and groups theses sub-trajectories on the basis of both spatial and temporal aspects by extending DBSCAN (Density Based Spatial Clustering of Applications with Noise) algorithm. We adopt the concepts of entropy and silhouette index to validate the clusters. Experiments conducted on two different real datasets demonstrate that the proposed clustering algorithm effectively discovers optimal clusters. Furthermore, experimental results reveal hidden and useful clusters and demonstrate that the proposed algorithm outperforms the CorClustST (Correlation-based Clustering of Big Spatiotemporal Datasets), and the ST-OPTICS (Spatiotemporal-Ordering Points to Identify Clustering Structure) algorithms.