An Improved Adaptive Self-Organizing Map

  • Dominik Olszewski
  • Janusz Kacprzyk
  • Sławomir Zadrożny
Part of the Studies in Computational Intelligence book series (SCI, volume 634)


We propose a novel adaptive Self-Organizing Map (SOM). In the introduced approach, the SOM neurons’ neighborhood widths are computed adaptively using the information about the frequencies of occurrences of input patterns in the input space. The neighborhood widths are determined independently for each neuron in the SOM grid. In this way, the proposed SOM properly visualizes the input data, especially, when there are significant differences in frequencies of occurrences of input patterns. The experimental study on real data, on three different datasets, verifies and confirms the effectiveness of the proposed adaptive SOM.


Self-organizing map Adaptive self-organizing map Neighborhood width Gaussian kernel Data visualization 



This contribution is supported by the Foundation for Polish Science under International PhD Projects in Intelligent Computing. Project financed from The European Union within the Innovative Economy Operational Programme 2007–2013 and European Regional Development Fund.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Dominik Olszewski
    • 1
  • Janusz Kacprzyk
    • 2
  • Sławomir Zadrożny
    • 2
  1. 1.Faculty of Electrical EngineeringWarsaw University of TechnologyWarsawPoland
  2. 2.Systems Research InstitutePolish Academy of SciencesWarsawPoland

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