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Conference Paper

An unsupervised clustering method using the entropy minimization


Kruggel,  Frithjof J.
MPI of Cognitive Neuroscience (Leipzig, -2003), The Prior Institutes, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Palubinskas, G., Descombes, X., & Kruggel, F. J. (1998). An unsupervised clustering method using the entropy minimization. In A. K. Jain (Ed.), Proceedings: Fourteenth International Conference on Pattern Recognition. Los Alamitos: IEEE Computer Society Press. doi: 10.1109/ICPR.1998.712082.

Cite as: https://hdl.handle.net/21.11116/0000-0003-4AC5-C
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.