Encyclopedia of Machine Learning

2010 Edition
| Editors: Claude Sammut, Geoffrey I. Webb

Self-Organizing Maps

  • Samuel Kaski
Reference work entry
DOI: https://doi.org/10.1007/978-0-387-30164-8_746

Synonyms

Definition

Self-organizing map (SOM), or Kohonen Map, is a computational data analysis method which produces nonlinear mappings of data to lower dimensions. Alternatively, the SOM can be viewed as a  clustering algorithm which produces a set of clusters organized on a regular grid. The roots of SOM are in neural computation (see  neural networks); it has been used as an abstract model for the formation of ordered maps of brain functions, such as sensory feature maps. Several variants have been proposed, ranging from dynamic models to Bayesian variants. The SOM has been used widely as an engineering tool for data analysis, process monitoring, and information visualization, in numerous application areas.
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Recommended Reading

  1. Bishop, C. M., Svensén, M., & Williams, C. K. I. (1998). GTM: The generative topographic mapping. Neural Computation, 10, 215–234.CrossRefGoogle Scholar
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  10. von der Malsburg, C. (1973). Self-organization of orientation sensitive cells in the striate cortex. Kybernetik, 14, 85–100.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Samuel Kaski

There are no affiliations available