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Related Experiment Videos

TreeSOM: Cluster analysis in the self-organizing map.

Elena V Samsonova1, Joost N Kok, Ad P Ijzerman

  • 1Division of Medicinal Chemistry, Leiden/Amsterdam Center for Drug Research, Leiden University, Einsteinweg 55, 2333CC Leiden, The Netherlands. elena.samsonova@liacs.nl

Neural Networks : the Official Journal of the International Neural Network Society
|June 20, 2006
PubMed
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This study introduces TreeSOM, a novel method for unsupervised clustering using Kohonen self-organizing maps (SOMs). It provides tools for cluster analysis, confidence assessment, and tree visualization, aiding in the selection of optimal SOMs.

Area of Science:

  • Data Science
  • Machine Learning
  • Computational Science

Background:

  • Clustering is crucial across scientific and engineering fields.
  • Kohonen self-organizing maps (SOMs) reduce dimensionality and group similar data points.
  • Current cluster analysis often requires manual user intervention.

Purpose of the Study:

  • To present the TreeSOM method for unsupervised SOM cluster analysis.
  • To introduce tools for determining cluster confidence and visualizing results.
  • To facilitate comparison with hierarchical classifiers and select optimal SOMs.

Main Methods:

  • Developed the TreeSOM algorithm for unsupervised clustering.
  • Implemented tools for cluster confidence assessment.
  • Introduced a distance measure for comparing cluster trees.

Related Experiment Videos

Main Results:

  • TreeSOM enables unsupervised analysis of SOMs.
  • The method visualizes results as a tree for easier interpretation.
  • A novel distance measure aids in selecting SOMs with high-confidence clusters.

Conclusions:

  • TreeSOM offers a robust framework for SOM-based clustering.
  • The developed tools enhance the interpretability and reliability of clustering results.
  • This approach facilitates the selection of SOMs with well-defined, confident clusters.