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TreeSOM: Cluster analysis in the self-organizing map.

Elena V Samsonova·Joost N Kok·Ad P Ijzerman
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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. 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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Age FactorsAlgorithmsAnimalsCalibrationCluster AnalysisComputational BiologyData Interpretation, StatisticalHumansNeural Networks, ComputerNumerical Analysis, Computer-AssistedPhylogenyProteinsTreesComparative StudyJournal ArticleResearch Support, Non-U.S. Gov't

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Elena V Samsonova, Joost N Kok, Ad P Ijzerman. 2006-06-15. TreeSOM: Cluster analysis in the self-organizing map.. https://doi.org/10.1016/j.neunet.2006.05.003

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