Spatiotemporal trajectory clustering: A clustering algorithm for spatiotemporal data. (15th September 2021)

• Record Type: Journal Article
• Authors: Ansari, Mohd Yousuf; Mainuddin, ; Ahmad, Amir; Bhushan, Gopal
• Is Part Of: Expert systems with applications. Volume 178(2021)
• Publication Date: 2021-09-15
• Subjects: Density-based clustering -- Trajectory clustering -- Spatiotemporal data -- Co-location events; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2021.115048
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Development of a novel spatiotemporal clustering algorithm for trajectory clustering. Implementation of visualization tool to validate the clusters. Experimentation on two different real datasets. The developed algorithm has ability to effectively discover quality clusters. This approach has many real-world applications. Abstract: 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.