Abstract
The paper presents two methods offering flexible solutions to cluster-analysis problems. The first one employs a genetic-algorithm-based Travelling-Salesman-Problem-solution, and the second one – self-organizing Kohonen networks. The operation of both techniques has been illustrated with the use of synthetic data set and then they have been tested by means of real-life, multidimensional Mushrooms Database (8124 records) available from the FTP server of the University of California at Irvine (ftp.ics.uci.edu).
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Gorzałczany, M.B., Rudziński, F. (2004). Application of Genetic Algorithms and Kohonen Networks to Cluster Analysis. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds) Artificial Intelligence and Soft Computing - ICAISC 2004. ICAISC 2004. Lecture Notes in Computer Science(), vol 3070. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24844-6_83
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DOI: https://doi.org/10.1007/978-3-540-24844-6_83
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22123-4
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