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

Abstract

Spatiotemporal 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.

Leveraging similarity analysis to understand variability in movement behavior

“…Future work can try to integrate more similarity measures with more perspectives to extend this framework. There are existing studies devoted to investigating the application scenarios of various similarity measures (Moayedi et al, 2019; Tao et al, 2021; Toohey & Duckham, 2015). For instance, Tao et al (2021) present a systematic exploration and comparison of some of the most important and commonly used similarity measures, including the Fréchet distance, DTW, LCSS, and edit distance discussed in this study.…”

A Novel Model for Landslide Displacement Prediction Based on EDR Selection and Multi-Swarm Intelligence Optimization Algorithm

“…The EDR, which is based on Levenshtein distance, is a traditional and well-established similarity measurement method proposed by Chen et al [45] and has been used for judging trajectory similarity since [48,49]. The EDR calculates the number of insertions, deletions, or replacement operations required to change the sequence R to T when the threshold is ε.…”

A quantitative method for the similarity assessment of typhoon tracks

“…Alemany (2019) rasterized the region of the Atlantic Ocean by a resolution of 1°× 1°, coded these grids with fixed numbers into which points of typhoon paths were categorized, and then predicted the grid number by an LSTM model. With the rapid development of geographic information systems (GISs), typhoon analysis studies are inspired with new methods in GIS (Moayedi et al 2019;Alizadeh et al 2020). Geographers have come up with the idea that the similarity between two typhoons is essential to typhoon research because the similarity analysis of historical typhoons is a great support and supplement to typhoon track prediction.…”