Implementation of a variable cluster self organising algorithm for high speed unsupervised pattern classification (lost in {0, 1}N space)

<p>Traditional neural networks for pattern classification use linear decisions to partition a multivalued high dimensional pattern space. This paper shows that the properties of binary space ({0, 1}N space) make it well suited for these tasks and a simple training algorithm is given. A simple measure of network ordering is used to allow a variable number of clusters and continuous learning.</p>