Abstract
This paper presents the usage of an artificial neural network, Kohonen’s self organizing feature map, for visualisation and classification of high dimensional data. Through a learning process, this neural network creates a mapping from a N-dimensional space to a two-dimensional plane of units (neurons). This mapping is known to preserve topological relations of the N-dimensional space. A specially developed technique, called U-matrix method has been developed in order to detect nonlinearities in the resulting mapping. This method can be used to visualize structures of the N-dimensional space. Boundaries between different subsets of input data can be detectet. This allows to use this method for a clustering of the data. New data can be classified in an associative way. It has been demonstrated, that the method can be used also for knowledge acquisition and exploratory data analysis purposes.
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.
Similar content being viewed by others
References
Grossberg, S. (1987), Competitive Learning: From Adaptive Activation to Adaptive Resonance, Cognitive Science, 17, 23–63.
Kohonen, T. (1982), Clustering, Taxonomy, and Topological Maps of Patterns, in: Lang, M. (Ed.), Proceedings of the Sixth International Conference on Pattern Recognition, Silver Spring, MD, IEEE Computer Society Press, 114–128.
Ritter, H., Martinez, T., Schulten, K. (1990), Neuronale Netze, Addison Wesley.
Rumelhart, D.E., McClelland, J.L. (1989), Parallel Distributed Processing: Explorations in the Microstructures of Cognition, Volume 1: Foundations, MIT Press, Cambridge.
Ultsch, A. (1991), Konnektionistische Modelle und ihre Integration mit wissensbasierten Systemen, Habilitationsschrift, Univ. Dortmund.
Ultsch, A. (1991a), The Integration of Neuronal Networks with Expert Systems, Proceedings Workshop on Industrial Applications of Neural Networks, Ascona, Vol III, 3–7.
Ultsch, A., Palm, G., Rückert, U. (1991a), Wissensverarbeitung in neuronaler Architektur, in: Brauer, Hernandez (Eds.): Verteilte künstliche Intelligenz und kooperatives Arbeiten, GI-Kongress, München, 508–518.
Ultsch, A., Hannuschka, R., Hartmann, U., Mandischer, M., Weber, V. (1991b), Optimizing Logical Proofs with Connectionist Networks, Proc. Intl. Conf. Artificial Neural Networks, Vol I, Helsinki, 585–590.
Ultsch, A., Halmans,G., Mantyk, R. (1991c), A Connectionist Knowledge Acquisition Tool: Concat, Proc. International Workshop on Artificial Intelligence and Statistics, January 2–5, Ft. Lauderdale.
Ultsch, A., Halmans, G. (1991), Data Normalization with Self-Organizing Feature Maps, Proc. Intl. Joint Conf. Neural Networks, Seattle, Vol I, 403–407.
Ultsch, A., Halmans, G. (1991a), Neuronale Netze zur Unterstützung der Umweltforschung, Symp. Computer Science for Environmental Protection, Munich.
Ultsch, A., Panda, PG. (1991), Die Kopplung konnektionistischer Modelle mit wissensbasierten Systemen, Tagungsband Expertensystemtage, Dortmund, VDI Verlag, 74–94.
Ultsch, A., Siemon, H.P. (1990), Kohonen’s Self Organizing Feature Maps for Exploratory Data Analysis, Proc. Intern. Neural Networks, Kluwer Academic Press, Paris, 305–308.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1993 Springer-Verlag Berlin · Heidelberg
About this paper
Cite this paper
Ultsch, A. (1993). Self-Organizing Neural Networks for Visualisation and Classification. In: Opitz, O., Lausen, B., Klar, R. (eds) Information and Classification. Studies in Classification, Data Analysis and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-50974-2_31
Download citation
DOI: https://doi.org/10.1007/978-3-642-50974-2_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-56736-3
Online ISBN: 978-3-642-50974-2
eBook Packages: Springer Book Archive