Modified Kohonen Networks for Complex Cluster-Analysis Problems
نویسندگان
چکیده
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 real-life, complex, multidimensional data set (Pen-Based Recognition of Handwritten Digits Database) available from the FTP server of the University of California at Irvine (ftp.ics.uci.edu).
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