Comparison of Similarity Measures for Trajectory Clustering - Aviation Use Case
Abstract
Various distance-based clustering algorithms have been reported, but the core component of all of them is a similarity or distance measure for classification of data. Rather than setting the priority to comparison of the performance of different clustering algorithms, it may be worthy to analyze the influence of different similarity measures on the results of clustering algorithms. The main contribution of this work is a comparative study of the impact of 9 similarity measures on similarity-based trajectory clustering using DBSCAN algorithm for commercial flight dataset. The novelty in this comparison is exploring the robustness of the clustering algorithm with respect to algorithm parameter. We evaluate the accuracy of clustering, accuracy of anomaly detection, algorithmic efficiency, and we determine the behavior profile for each measure. We show that DTW and Frechet distance lead to the best clustering results, while LCSS and Hausdorff Cosine should be avoided for this task.
Keywords
similarity measure, Clustering, comparison, aviationThis work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
M. Todoric and T. Mastelic, "Comparison of Similarity Measures for Trajectory Clustering - Aviation Use Case," in Journal of Communications Software and Systems, vol. 19, no. 2, pp. 178-187, June 2023, doi: https://doi.org/10.24138/jcomss-2022-0116
@article{todoric2023comparisonsimilarity, author = {Marija Todoric and Toni Mastelic}, title = {Comparison of Similarity Measures for Trajectory Clustering - Aviation Use Case}, journal = {Journal of Communications Software and Systems}, month = {6}, year = {2023}, volume = {19}, number = {2}, pages = {178--187}, doi = {https://doi.org/10.24138/jcomss-2022-0116}, url = {https://doi.org/https://doi.org/10.24138/jcomss-2022-0116} }