In artificial neural networks [Bis95,MMR97], the input passes
through a connected network of simple processing units called neurons
to the output. Often, each neuron represents a function
which maps several inputs to a single output by a
weighted summation and non-linear transformation step. In competitive
networks (such as Hamming network, MaxNet) a neuron has excitory
effect on itself and inhibitory effects on its neighbors. For
example, the max-function and -means clustering can be expressed as
a winner-take-all network. An adaptive approach that allows for a
variable number of clusters and is controlled by a vigilance parameter
is Adaptive Resonance Theory (ART) [CG88]. The
most popular networks for clustering are topologically organized
networks. The Self-Organizing (feature) Map (SOM)
was proposed by Kohonen [Koh95] and fits a predefined
network structure to the data in a topology preserving fashion by
updating the winner *and* its adjacent neighbors.

Alexander Strehl 2002-05-03