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Free keywords:
Clustering methods, Entropy, Minimization methods, Clustering algorithms, Histograms, Bayesian methods, Level set, Neuroscience, Simulated annealing
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Robustness
Abstract:
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.
