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ThinkMind // International Journal On Advances in Software, volume 9, numbers 3 and 4, 2016 // View article soft_v9_n34_2016_9


Unsupervised curves clustering by minimizing entropy: implementation and application to air traffic

Authors:
Florence Nicol
Stéphane Puechmorel

Keywords: curve clustering; probability distribution estimation; functional statistics; minimum entropy; Lie group modeling; air traffic management.

Abstract:
In many applications such as Air Traffic Management (ATM), clustering trajectories in groups of similar curves is of crucial importance. When data considered are functional in nature, like curves, dedicated algorithms exist, mostly based on truncated expansion on Hilbert basis. When additional constraints are put on the curves, as 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applications related to air traffic where operational considerations are to be taken into account, usual procedures are no longer applicable.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 and its implementation is discussed in detail, especially the computation of the curve system density and the entropy minimization by a gradient descent algorithm. This algorithm yields 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.

Pages: 260 to 271

Copyright: Copyright (c) to authors, 2016. Used with permission.

Publication date: December 31, 2016

Published in: journal

ISSN: 1942-2628

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