Clustering Based on Neural Network Processing

Artificial neural networks — having been popular in the fifties and sixties — recently received a new wave of interest (Anderson 1986; Materna 1987; Fahlman Sz Hinton 1987). Neural network computing, also known as connectionism, is inspired from brain theory and is based on interconnecting a large number of simple processing elements called ‘neurons’, which cooperate in the computations. Essentially such a neuron adds up the weighted input on its input line, pushes the resulting net input through a (possibly non-linear) transfer function and communicates the output via its output lines to all other neurons to which it is connected. Mathematically the static part of a neural network is nothing but a ‘network’ in graph theory, i.e. a labelled weighted directed graph, whose vertices correspond to the neurons and whose weighted arcs represent the network connections. A network state corresponds to a set of weights for the vertices. The static description of a neural network is complemented by the specification of the dynamics governing its state changes.