K-means clustering algorithm using entropy

Gintautas Palubinskas

doi:10.1117/12.331894

4 December 1998 K-means clustering algorithm using entropy

Gintautas Palubinskas

Proceedings Volume 3500, Image and Signal Processing for Remote Sensing IV; (1998) https://doi.org/10.1117/12.331894
Event: Remote Sensing, 1998, Barcelona, Spain

Abstract

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 gray level description. Therefore we minimize the entropy associated with the clustering histogram. This enables us to overcome the problem of defining a priori the number of clusters and an initialization 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.

Citation Download Citation

Gintautas Palubinskas "K-means clustering algorithm using entropy", Proc. SPIE 3500, Image and Signal Processing for Remote Sensing IV, (4 December 1998); https://doi.org/10.1117/12.331894

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