Self-organizing maps , visualization

The Kohonen network or self-organizing map (SOM) was developed by Teuvo Kohonen [11]. It can be used to classify a set of input vectors according to their similarity. The result of such a network is usually a two-dimensional map. Thus, the Kohonen network is a method for projecting objects from a multidimensional space into a two-dimensional space. This projection keeps the topology of the multidimensional space, i.e., points which are close to one another in the multidimensional space are neighbors in the two-dimensional space as well. An advantage of this method is that the results of such a mapping can easily be visualized. 

Rantanen, J.T. Laine, S.J. Antikainen, O.K. etal, Visualization of fluid-bed granulation with self-organizing maps /. Pharm. Biomed. Anal. 2001, 24, 343-352. 

Jirapummin, C., and N. Wattanapongsakorn, Visual Intrusion Detection using Self-Organizing Maps, Proc. of Electrical and Electronic Conference (EECON-24), Thailand, Vol. 2, pp. 1343-1349,2001. 

The next step was to use a Kohonen two-dimensional self-organizing map to represent the spectral data in the 218-dimensional measurement space. Self-organizing maps are, for the most part, used to visualize high-dimensional data. However, classification and prediction of multivariate data can also be performed with these... 

It can be seen that the two different sets of molecules separate quite well, however class membership was not used in training the network. Class membership served only for the visualization of the self-organizing map after training. Thus, effects that are responsible for the different binding of dopamine and benzodiazepine agonists are reproduced by the chosen structure representation. 

MuSA.RT has been used to analyze and visualize music by Pachelbel, Bach, and Barber (Chew and Frangois, 2005). These visualizations have been presented in juxtaposition to Sapp s keyspaces (2005) and Toiviainen s self-organizing maps (2005). MuSA.RT has also been demonstrated internationally and was presented at the AAAS Ig Nobel session in 2008. 

Toiviainen, P. 2005. Visualization of tonal content with self-organizing maps and self-similarity matrices. ACM Computers in Entertainment 3(4) 10 pages. 

In addition to fuzzy logic, we will concentrate on the Self-Organizing Map (SOM) algorithm, since it has properties that make it both a data visualization and a clustering technique. We present this method in relation to other... 

Neural nets can also be based on unsupervised learning strategies. To date these nets have been employed primarily to support data visualization, but their flexibility is such that they are becoming more common in a wide variety of applications. A simple version of an unsupervised neural net is the Kohonen self-organizing map (SOM) (Kohonen, 1982, 1984 Lang, this volume). These nets also use a set number of nodes, but operate according to different principles. 

Self-organizing maps were first used to visualize collections of molecules by Gasteiger et al. at the Uni-... 

Self-organizing Kohonen maps are a preferred method for visualization of these redundancies. Product-based library selection is a powerful tool to select fractions of a virtual library in order to complete a homogeneous covering of the chemical space. 

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]. 

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