SelfOrganizing Maps
 Samuel Kaski
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Synonyms
Kohonen maps; Selforganizing feature maps; SOM
Definition
Selforganizing 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.SelfOrganizing Maps. Figure 1
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 Fort, JC (2006) SOM’s mathematics. Neural Networks 19: pp. 812816 CrossRef
 Hastie, T, Tibshirani, R, Friedman, J (2001) The elements of statistical learning. Springer, New York
 Heskes, T (2001) Selforganizing maps, vector quantization, and mixture modeling. IEEE Transactions on Neural Networks 12: pp. 12991305 CrossRef
 Kohonen, T (1982) Selforganized formation of topologically correct feature maps. Biological Cybernetics 43: pp. 5969 CrossRef
 Kohonen, T (2001) Selforganizing maps. Springer, Berlin
 Kohonen, T, Kaski, S, Lagus, K, Salojärvi, J, Honkela, J, Paatero, V (2000) Self organization of a massive document collection. IEEE Transactions on Neural Networks 11: pp. 574585 CrossRef
 Luttrell, SP (1994) A Bayesian analysis of selforganizing maps. Neural Computation 6: pp. 767794 CrossRef
 Pöllä, M., Honkela, T., & Kohonen, T. (2009). Bibliography of selforganizing map (SOM) papers: 20022005 addendum. Report TKKICSR23, Helsinki University of Technology, Department of Information and Computer Science, Espoo, Finland.
 Malsburg, C (1973) Selforganization of orientation sensitive cells in the striate cortex. Kybernetik 14: pp. 85100 CrossRef
 Title
 SelfOrganizing Maps
 Reference Work Title
 Encyclopedia of Machine Learning
 Pages
 pp 886888
 Copyright
 2010
 DOI
 10.1007/9780387301648_746
 Print ISBN
 9780387307688
 Online ISBN
 9780387301648
 Publisher
 Springer US
 Copyright Holder
 Springer Science+Business Media, LLC
 Additional Links
 Topics
 Industry Sectors
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 Editors

 Claude Sammut ^{(221)}
 Geoffrey I. Webb ^{(222)}
 Editor Affiliations

 221. School of Computer Science and Engineering, University of New South Wales
 222. Faculty of Information Technology, Clayton School of Information Technology, Monash University
 Authors
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