Comparing self-organizing maps

  • Samuel Kaski
  • Krista Lagus
Poster Presentations 3 Theory V: Self-Organizing Maps
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1112)


In exploratory analysis of high-dimensional data the self-organizing map can be used to illustrate relations between the data items. We have developed two measures for comparing how different maps represent these relations. The other combines an index of discontinuities in the mapping from the input data set to the map grid with an index of the accuracy with which the map represents the data set. This measure can be used for determining the goodness of single maps. The other measure has been used to directly compare how similarly two maps represent relations between data items. Such a measure of the dissimilarity of maps is useful, e.g., for analyzing the sensitivity of maps to variations in their inputs or in the learning process. Also the similarity of two data sets can be compared indirectly by comparing the maps that represent them.


Data Item Input Space Dissimilarity Measure Exploratory Data Analysis Learning Parameter 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • Samuel Kaski
    • 1
  • Krista Lagus
    • 1
  1. 1.Neural Networks Research CentreHelsinki University of TechnologyEspooFinland

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