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Developments and applications of the self-organizing map and related algorithms

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  • Kangas, Jari
  • Kohonen, Teuvo

Abstract

In this paper the basic principles and developments of an unsupervised learning algorithm, the self-organizing map (SOM) and a supervised learning algorithm, the learning vector quantization (LVQ) are explained. Some practical applications of the algorithms in data analysis, data visualization and pattern recognition tasks are mentioned. At the end of the paper new results are reported about increased error tolerance in the transmission of vector quantized images, provided by the topological ordering of codewords by the SOM algorithm.

Suggested Citation

  • Kangas, Jari & Kohonen, Teuvo, 1996. "Developments and applications of the self-organizing map and related algorithms," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 41(1), pages 3-12.
    • Handle: RePEc:eee:matcom:v:41:y:1996:i:1:p:3-12
    DOI: 10.1016/0378-4754(96)88223-1
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    Cited by:

    1. Qin, Rui & Liu, Yan-Kui, 2010. "Modeling data envelopment analysis by chance method in hybrid uncertain environments," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 80(5), pages 922-950.
    2. Dhan Lord B. Fortela & Matthew Crawford & Alyssa DeLattre & Spencer Kowalski & Mary Lissard & Ashton Fremin & Wayne Sharp & Emmanuel Revellame & Rafael Hernandez & Mark Zappi, 2020. "Using Self-Organizing Maps to Elucidate Patterns among Variables in Simulated Syngas Combustion," Clean Technol., MDPI, vol. 2(2), pages 1-14, April.

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