Discussion on density-based clustering methods applied for automated identification of airspace flows

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dc.contributor.author Verdonk Gallego, Christian Eduardo
dc.contributor.author Gómez Comendador, Victor Fernando
dc.contributor.author Saez Nieto, Francisco Javier
dc.contributor.author Garcia Martinez, Miguel
dc.date.accessioned 2019-02-22T13:53:17Z
dc.date.available 2019-02-22T13:53:17Z
dc.date.issued 2018-12-10
dc.identifier.citation Verdonk 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 2018 en_UK
dc.identifier.isbn 978-1-5386-4112-5
dc.identifier.issn 2155-7209
dc.identifier.uri https://doi.org/10.1109/DASC.2018.8569219
dc.identifier.uri https://dspace.lib.cranfield.ac.uk/handle/1826/13932
dc.description.abstract Air 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. en_UK
dc.language.iso en en_UK
dc.publisher IEEE en_UK
dc.rights Attribution-NonCommercial 4.0 International *
dc.rights.uri http://creativecommons.org/licenses/by-nc/4.0/ *
dc.subject density-based clustering en_UK
dc.subject air traffic flows en_UK
dc.subject machine learning en_UK
dc.subject air traffic management en_UK
dc.title Discussion on density-based clustering methods applied for automated identification of airspace flows en_UK
dc.type Conference paper en_UK

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