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An unsupervised clustering method using the entropy minimization

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3 Author(s)
Palubinskas, G. ; Deutsches Zentrum fur Luft- und Raumfahrt (DLR) e.V., Wessling, Germany ; Descombes, X. ; Kruggel, F.

We address the problem of unsupervised clustering using a Bayesian framework. The entropy is considered to define a priori and enables one to overcome the problem of defining a priori the number of clusters and an initialization of their centers. A deterministic algorithm derived from the standard k-means algorithm is proposed and compared with simulated annealing algorithms. The robustness of the proposed method is shown on a magnetic resonance images database containing 65 volumetric (3D) images

Published in:

Pattern Recognition, 1998. Proceedings. Fourteenth International Conference on  (Volume:2 )

Date of Conference:

16-20 Aug 1998