Self-Organizing Neural Networks for Visualisation and Classification

Abstract

This paper presents the usage of an artificial neural network, Kohonen’s self organizing feature map, for visualisation and classification of high dimensional data. Through a learning process, this neural network creates a mapping from a N-dimensional space to a two-dimensional plane of units (neurons). This mapping is known to preserve topological relations of the N-dimensional space. A specially developed technique, called U-matrix method has been developed in order to detect nonlinearities in the resulting mapping. This method can be used to visualize structures of the N-dimensional space. Boundaries between different subsets of input data can be detectet. This allows to use this method for a clustering of the data. New data can be classified in an associative way. It has been demonstrated, that the method can be used also for knowledge acquisition and exploratory data analysis purposes.