Modified Kohonen networks for complex cluster-analysis problems

Abstract

The paper presents a modification of the self-organizing Kohonen networks for more efficient coping with complex, multidimensional cluster-analysis problems. The essence of modification consists in allowing the neuron chain - as the learning progresses - to disconnect and later to reconnect again. First, the operation of the modified approach has been illustrated by means of synthetic data set. Then, this technique has been tested with the use of a real-life, complex, multidimensional data set (Pen-Baaed Recognition of Handwritten Digits Database) available from the FTP server of the University of California at Irvine (ftp.ics.uci.edu).