An Unsupervised Clustering Method Using the Entropy Minimization

Authors:
Affiliations:
Venue:
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Year:
1998

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Abstract

We address the problem of unsupervised clustering using a Bayesian framework. The entropy is considered to define a prior and enables us 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 (MR) images database containing 65 volumetric (3D) images.