this paper is to provide an introduction to Adaptive Resonance Theory (ART) by examining ART-1, the first member of the family of ART neural networks. The only prerequisite knowledge in the area of neural networks necessary for understanding this paper is backpropagation [Hinton86]. For an easy introduction to neural networks see [Freeman91], for a more in depth overview of the field see [Hertz91]. Many interesting problems concern the classification of data. For example, say we want to classify animals according to certain characteristics described by a set of parameters. We might have a dog, a cat and an owl. Some characteristics might be "number of legs", "can fly", "has fur" and "is a carnivore". With these characteristics we would hope that the cat and the dog are classified together and the owl separately. In this paper an algorithm which performs this mapping is called a clustering algorithm. A clustering algorithm takes as input a set of input vectors and gives as output a set of clusters and a mapping of each input vector to a cluster. Input vectors which are close to each other according to a specific similarity measure should be mapped to the same cluster. Clusters can be labelled to indicate a particular semantic meaning pertaining to all input vectors mapped to that cluster. The cat and the dog might be classified in a cluster labelled "mammals" and the owl in "birds". However one could also choose "pets" as label for the cluster with the cat and the dog and "winged animal" for the other. Clusters are usually internally represented using prototype vectors which are vectors indicating a certain similarity between the input vectors which are mapped to a cluster. In the above example the first cluster might have prototype vector (4 legs,can't fly,has fur,is a ..
