Unsupervised and supervised data clustering with competitive neural networks
Joachim M. Buhmann, Hans Kühnel · 2003
The authors discuss objective functions for unsupervised and supervised data clustering and the respective competitive neural networks which implement these clustering algorithms. They propose a cost function for unsupervised and supervised data clustering which comprises distortion costs, complexity costs and supervision costs. A maximum entropy estimation of the clustering cost function yields an optimal number of clusters, their positions and their cluster probabilities. A three-layer neural network with a winner-take-all connectivity in the clustering layer implements the proposed algorithm.>