A constructive unsupervised learning algorithm for clustering binary patterns

We propose a constructive unsupervised learning algorithm (CULA) for Boolean neural networks based on geometrical expansion. CULA constructs two-layered (input and output layer) neural networks. We visualize output neurons in terms of hyperspheres. CULA results in fast learning because it determines whether to add a new coming vertex to a neuron by its geometrical location, not by iterant computation. We illustrate CULA by using 101 instances in zoo database of Richard Forsyth, and compare our unsupervised clustering with clustering by biological experts given in the zoo database.