Neural clustering-implementation of clustering model using neural networks
M. Sato, Y. Sato · 2002
This paper proposes a general class of clustering model, in which aggregation operators are used to define the degree of simultaneous belongingness of a pair of objects to a cluster. Moreover, we show that the fitting algorithm is implemented by using neural networks. It naturally follows that this implementation is proven by the universal approximation theorem.