Unsupervised and supervised data clustering with competitive neural networks

A cost function for unsupervised and supervised data clustering which comprises distortion costs, complexity costs and supervision costs is proposed and a maximum entropy estimation of the clustering cost function yields an optimal number of clusters, their positions and their cluster probabilities.

Abstract

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.>