Verdonk Gallego, Christian EduardoGómez Comendador, Victor FernandoSaez Nieto, Francisco JavierGarcia Martinez, Miguel2019-02-222019-02-222018-12-10Verdonk Gallego CE, Gómez Comendador VF, Sáez Nieto FJ & García Martínez M (2018) Discussion on density-based clustering methods applied for automated identification of airspace flows. In: 2018 IEEE/AIAA 37th Digital Avionics Systems Conference (DASC), London, 23-27 September 2018978-1-5386-4112-52155-7209https://doi.org/10.1109/DASC.2018.8569219https://dspace.lib.cranfield.ac.uk/handle/1826/13932Air Traffic Management systems generate a huge amount of track data daily. Flight trajectories can be clustered to extract main air traffic flows by means of unsupervised machine learning techniques. A well-known methodology for unsupervised extraction of air traffic flows conducts a two-step process. The first step reduces the dimensionality of the track data, whereas the second step clusters the data based on a density-based algorithm, DBSCAN. This paper explores advancements in density-based clustering such as OPTICS or HDBSCAN*. This assessment is based on quantitative and qualitative evaluations of the clustering solutions offered by these algorithms. In addition, the paper proposes a hierarchical clustering algorithm for handling noise in this methodology. This algorithm is based on a recursive application of DBSCAN* (RDBSCAN*). The paper demonstrates the sensitivity of these algorithms to different hyper-parameters, recommending a specific setting for the main one, which is common for all methods. RDBSCAN* outperforms the other algorithms in terms of the density-based internal validity metric. Finally, the outcome of the clustering shows that the algorithm extracts main clusters of the dataset effectively, connecting outliers to these main clusters.enAttribution-NonCommercial 4.0 Internationalhttp://creativecommons.org/licenses/by-nc/4.0/density-based clusteringair traffic flowsmachine learningair traffic managementConference paper