Reliable hierarchical clustering with the self-organizing map

Authors:
Elena V. Samsonova;Thomas Bäck;Joost N. Kok;Ad P. IJzerman
Affiliations:
Leiden / Amsterdam Center for Drug Research, Leiden University, Leiden, The Netherlands;Leiden Institute for Advanced Computer Science, Leiden University, Leiden, The Netherlands;Leiden Institute for Advanced Computer Science, Leiden University, Leiden, The Netherlands;Leiden / Amsterdam Center for Drug Research, Leiden University, Leiden, The Netherlands
Venue:
IDA'05 Proceedings of the 6th international conference on Advances in Intelligent Data Analysis
Year:
2005

Citing 5
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Abstract

Clustering problems arise in various domains of science and engineering. A large number of methods have been developed to date. 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 a method 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 to select a SOM with the most confident clusters.