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 - selforganizing 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).
Cite
CITATION STYLE
Gorzalczany, M. B., & Rudziński, F. (2004). Application of genetic algorithms and kohonen networks to cluster analysis. In Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) (Vol. 3070, pp. 556–561). Springer Verlag. https://doi.org/10.1007/978-3-540-24844-6_83
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