Abstract
In this work we introduce a method for classification and visualization. In contrast to simultaneous methods like e.g. Kohonen SOM this new approach, called KMC/EDAM, runs through two stages. In the first stage the data is clustered by classical methods like K-means clustering. In the second stage the centroids of the obtained clusters are visualized in a fixed target space which is directly comparable to that of SOM.
This work has been supported by the Deutsche Forschungsgemeinschaft, Sonder-forschungsbereich 475.
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
ANDERBERG, M.R. (1973): Cluster Analysis for Applications. Academic Press Inc., New York.
BEZDEK, J.C., PAL, N.R. (1995): An index of topological preservation for feature extraction. Pattern Recognition, 28/3, 381–391.
BOCK, H.H. (1997): Simultaneous visualization and clustering methods as an alternative to Kohonen maps. In: G. Della Riccia, R. Kruse and H.-J. Lenz(Eds.): Learnings, networks and statistics. CISM Courses and Lectures, 382, Springer, New York, 67–85.
FISHER, R.A. (1936): The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7/2, 179–188.
HAMERLE, A., PAPE, H. (1996): Grundlagen der mehrdimensionalen Skalierung. In: L. Fahrmeir, A. Hamerle, and G. Tutz (Eds.): Multivariate statistische Verfahren. De Gruyter, Berlin 765–792.
KAUFMANN, H., PAPE, H. (1996): Clusteranalyse. In: L. Fahrmeir, A. Hamerle, and G. Tutz (Eds.): Multivariate statistische Verfahren. De Gruyter, Berlin 437–536.
KOHONEN, T. (1990): The Self-Organizing Map. Proceedings of the IEEE, 78/9, 1464–1480.
R DEVELOPMENT CORE TEAM (2004): R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org
ULTSCH, A. (2003): Maps for the visualization of high-dimensional data spaces. Proc. Workshop on Self organizing Maps, 225–230.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin · Heidelberg
About this paper
Cite this paper
Raabe, N., Luebke, K., Weihs, C. (2005). KMC/EDAM: A New Approach for the Visualization of K-Means Clustering Results. In: Weihs, C., Gaul, W. (eds) Classification — the Ubiquitous Challenge. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-28084-7_21
Download citation
DOI: https://doi.org/10.1007/3-540-28084-7_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25677-9
Online ISBN: 978-3-540-28084-2
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)