An evaluation of the efficiency of similarity functions in density-based clustering of spatial trajectories

Abstract

ABSTRACTSpatiotemporal movement pattern discovery has stimulated considerable interest due to its numerous applications, including data analysis, machine learning, data segmentation, data reduction, abnormal behaviour detection, noise filtering, and pattern recognition. Trajectory clustering is among the most widely used approaches of extracting interesting patterns in large trajectory datasets. In this paper, regarding the optimal performance of density-based clustering, we present a comparison between eight similarity measures in density-based clustering of moving objects’ trajectories. In particular, Distance Functions such as Euclidean, L1, Hausdorff, Fréchet, Dynamic Time Warping (DTW), Longest Common SubSequence (LCSS), Edit Distance on Real signals (EDR), and Edit distance with Real Penalty (ERP) are applied in DBSCAN on three different datasets with varying characteristics. Also, experimental results are evaluated using both internal and external indices. Furthermore, we propose two modified validation measures for density-based trajectory clustering, which can deal with arbitrarily shaped clusters with different densities and sizes. These proposed measures were aimed at evaluating trajectory clusters effectively in both spatial and spatio-temporal aspects. The evaluation results show that choosing an appropriate Distance Function is dependent on data and its movement parameters. However, in total, Euclidean distance proves to show superiority over the other Distance Functions regarding the Purity index and EDR distance can provide better performance in terms of spatial and spatio-temporal quality of clusters. Finally, in terms of computation time and scalability, Euclidean, L1, and LCSS are the most efficient Distance Functions.