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

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dc.contributor.authorVerdonk Gallego, Christian Eduardo
dc.contributor.authorGómez Comendador, Victor Fernando
dc.contributor.authorSaez Nieto, Francisco Javier
dc.contributor.authorGarcia Martinez, Miguel
dc.date.accessioned2019-02-22T13:53:17Z
dc.date.available2019-02-22T13:53:17Z
dc.date.issued2018-12-10
dc.description.abstractAir 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.identifier.citationVerdonk 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 2018en_UK
dc.identifier.isbn978-1-5386-4112-5
dc.identifier.issn2155-7209
dc.identifier.urihttps://doi.org/10.1109/DASC.2018.8569219
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/13932
dc.language.isoenen_UK
dc.publisherIEEEen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectdensity-based clusteringen_UK
dc.subjectair traffic flowsen_UK
dc.subjectmachine learningen_UK
dc.subjectair traffic managementen_UK
dc.titleDiscussion on density-based clustering methods applied for automated identification of airspace flowsen_UK
dc.typeConference paperen_UK

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