TreeSOM: cluster analysis in the self-organizing map Clustering problems arise in various domains of science and engineering. A large number of methods have been developed to date. The Kohonen self-organizing map (SOM) is a popular tool that maps a high-dimensional space onto a small number of dimensions by placing similar elements close together, forming clusters. Cluster analysis is often left to the user. In this paper we present the method TreeSOM and a set of tools to perform unsupervised SOM cluster analysis, determine cluster confidence and visualize the result as a tree facilitating comparison with existing hierarchical classifiers. We also introduce a distance measure for cluster trees that allows one to select a SOM with the most confident clusters.
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References in zbMATH (referenced in 5 articles , 1 standard article )
Showing results 1 to 5 of 5.
- Doan, Nhat-Quang; Azzag, Hanane; Lebbah, Mustapha: Growing self-organizing trees for autonomous hierarchical clustering (2013)
- Landis, Florian; Ott, Thomas; Stoop, Ruedi: Hebbian self-organizing integrate-and-fire networks for data clustering (2010)
- Furukawa, Tetsuo: SOM of SOMs (2009) ioport
- Samsonova, Elena V.; Kok, Joost N.; Ijzerman, Ad P.: TreeSOM: cluster analysis in the self-organizing map (2006)
- Samsonova, Elena V.; Bäck, Thomas; Kok, Joost N.; IJzerman, Ad P.: Reliable hierarchical clustering with the self-organizing map (2005)