Self-organizing feature maps network Kohonen networks

Meissen, W. J., Smits, J. R. M., Buydens, L. M. C., and Kateman, G. (1994) Using artificial neural networks for solving chemical problems Part II. Kohonen self-organizing feature maps and Hopfield networks. Chemom. Intell. Lab. Syst. 23,267-291. 

A specialized method for similarity-based visualization of high-dimensional data is formed by self-organizing feature maps (SOM). The data items are arranged on a two-dimensional plane with the aid of neural networks, especially Kohonen nets. Similarity between data items is represented by spacial closeness, while large distances indicate major dissimilarities [968]. At the authors department, a system called MIDAS had already been developed which combines strategies for the creation of feature maps with the supervised generation of fuzzy-terms from the maps [967]. 

In contrast to common ANNs, Kohonen networks produce self-organized topological feature maps (Kohonen [1982, 1984]). The basic idea of Kohonen mapping is that information in data usually contains not only an algebraic but also a topological aspect. These double aspect is shown schematically in Fig. 8.25 where the data and the structure of them are composed. 

Self-organizing maps (also called SOMs, Kohonen feature maps, or kmaps) are special kinds of artificial neural networks (ANNs) that are able to represent sets of descriptors in a low-dimensional map [114—116], and are increasingly applied for mapping of various molecular data in the fields of analytical chemistry and drug design [89, 117, 118]. 

Kohonen networks are also termed self-organizing nets or self-organizing feature maps. 

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