Abstract
We consider the visualization of multivariate data yielded by the SOM and the GTM methods. Special emphasis is put on the position of outliers and extreme points. When evaluating four data sets, it is found that: The GTM method yields decidedly smaller quantization errors and much higher topological errors as the SOM does. Generally, the topology of both representations looks similar.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bartkowiak A., Szustalewicz A., Evelpidou N., Vassilopoulos A. (2003). Choosing data vectors representing a huge data set: a comparison of Kohonen’s maps and the neural gas method. Proc. First Int. Conf. on Environmental Research and Assessment, Bucharest, Romania, pp. 561–572, print on CD—ROM, ©Ars Docendi P. H, Bucharest, Romania.
Bartkowiak A. (2003). SOM and GTM: Comparison in Figures. Report December 2003. http://www.ii.uni.wroc.pl/“aba/papers.html
Bishop C.M., Svensèn M., and Williams C. K. I. (1996). The generative topographic mapping. Neural Computation 10 (1), pp. 215–235.
Kiviluoto K. (1996). Topology preservation in self-organizing maps. Proceedings ICNN’96, V. 1, IEEE Neural Networks Council, June 1996, Piscataway, New Jersey, USA, pp. 294–299.
Kohonen T. (1995). Self-Organizing Maps. Springer Series in Information Sciences, V. 30, Berlin.
Nabney I.T. (2001). Netlab: Algorithms for Pattern Recognition. Springer London, Berlin, Heidelberg. Springer Series: Advances in Pattern Recognition.
Rousseeuw P.J., van Driessen K. (1999). A fast algorithm for the minimum covariance determinant. Technometrics 41, pp. 212–223.
Venna J., Kaski S. (2001). Neighborhood preservation in nonlinear projection methods: An experimental study. In: Proceedings ICANN 2001, Vienna. Edited by G. Dorffner, H. Bischof, and K. Hornik. Springer, Berlin, pp. 485–491.
Vesanto J. (1999). SOM—based data visualizing methods. Intelligent Data Analysis, 3 (2), pp. 111–126.
Vesanto J., Himberg J., et al. (2000). SOM Toolbox for Matlab 5. SOM Toolbox Team, HUT, Finland, Libella Oy, Espoo, pp. 1–54. http://www.cis.hut.fi/projects/somtoolbox, Version Obeta 2.0, Nov. 2001.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bartkowiak, A. (2004). Visualizing large data by the SOM and GTM methods — what are we obtaining?. In: Kłopotek, M.A., Wierzchoń, S.T., Trojanowski, K. (eds) Intelligent Information Processing and Web Mining. Advances in Soft Computing, vol 25. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39985-8_41
Download citation
DOI: https://doi.org/10.1007/978-3-540-39985-8_41
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-21331-4
Online ISBN: 978-3-540-39985-8
eBook Packages: Springer Book Archive