Minimum entropy unsupervised aircraft trajectories clustering: theory and implementation

Abstract : Clustering is a common operation in statistics. When data considered are functional in nature, like curves, dedicated algorithms exist, mostly based on truncated expansions on Hilbert basis. When additional constraints are put on the curves, like in applications related to air traffic where operational considerations are to be taken into account, usual procedures are no longer applicable. A new approach based on entropy minimization and Lie group modeling is presented here, yielding an efficient unsupervised algorithm suitable for automated traffic analysis. It outputs cluster centroids with low curvature, making it a valuable tool in airspace design applications or route planning.
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Soumis le : vendredi 16 septembre 2016 - 14:10:14
Dernière modification le : mardi 20 septembre 2016 - 09:56:40
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Florence Nicol, Stéphane Puechmorel. Minimum entropy unsupervised aircraft trajectories clustering: theory and implementation. 2016. 〈hal-01367593〉

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