Abstract: We address the problem of unsupervised clustering using a Bayesian framework. The entropy is considered to define a priori and enables one to ...
Unsupervised clustering methods such as popular ones: k-means, fuzzy c-means and the maximum like- lihood with expectation maximization require an ini-.
Unsupervised clustering methods such as popular ones: k-means, fuzzy c-means and the maximum like- lihood with expectation maximization require an ini-.
We address the problem of unsupervised clustering using a Bayesian mference. The entropy is considered to define a prior and enables us to overcome the ...
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What is entropy minimization?
What is entropy in clustering?
AN UNSUPERVISED CLUSTERING METHOD. BY ENTROPY MINIMIZATION. G. PALUBINSKAS ... posed using entropy either by maximising the information between two clusters.
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 ...
Gintautas Palubinskas, Xavier Descombes, Frithjof Kruggel: An unsupervised clustering method using the entropy minimization. ICPR 1998: 1816-1818.
Bibliography of Software Language Engineering in Generated Hypertext (BibSLEIGH) is created and maintained by Dr. Vadim Zaytsev. Hosted as a part of SLEBOK on ...
Feb 9, 2013 · Entropy Minimization is a new clustering algorithm that works with both categorical and numeric data, and scales well to extremely large data sets.