K-means clustering algorithm using the entropy

The problem of unsupervised clustering of data is formulated using a Bayesian inference. The entropy is considered to define a prior. In clustering problem we have to reduce the complexity of the grey level description. Therefore we minimise the entropy associated with the clustering histogram. This enables us to overcome the problem of defining a priori the number of clusters and an initialisation of their centers. Under the assumption of a normal distribution of data the proposed clustering method reduces to a deterministic algorithm (very fast) which appears to be an. extension of the standard k-means clustering algorithm. Our model depends on a parameter weighting the prior term and the goodness of fit term. This hyper-parameter allows us to define the coarseness of the clustering and is data independent. Heuristic argument is proposed to estimate this parameter. The new clustering approach was successfully tested on a database of 65 magnetic resonance images and remote sensing images.