An unsupervised clustering method using the entropy minimization
Gintautas Palubinskas, Xavier Descombes, Frithjof Kruggel · 2002
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.