Reference work entry
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.
- Pöllä, M., Honkela, T., & Kohonen, T. (2009). Bibliography of self-organizing map (SOM) papers: 2002-2005 addendum. Report TKK-ICS-R23, Helsinki University of Technology, Department of Information and Computer Science, Espoo, Finland.Google Scholar
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