Visual Exploration of Numeric Data Using 3D Self Organizing Feature Maps


Scientific data visualization techniques are used to transform large quantities of numeric data vectors into colorized patterns and geometric shapes that enable scientists and engineers to observe the underlying behaviour of complex systems in new informative ways. In this paper a three-dimensional graphical form, called a “glyph”, is created by a self-organizing feature map (SOFM) and used to visually display patterns inherent in large volume N-dimensional data sets. The level of similarity amongst the input vectors assigned to a common lattice node, or neighboring nodes, will determine the local shape of the glyph. Colour coding is used to show prior vector classification by an expert. Benign and malignant breast cancer data are used to illustrate how this approach can automatically create colorized glyphs for assisting the data analyst to explore the database for meaningful patterns.

  • Abstract
  • Introduction
  • Exploratory Clustering Using the SOFM
  • Creating a Glyph Using a 3D SOFM
  • Experimental Study
  • Conclusions
  • Acknowledgements
  • References

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