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V1OUK1FN.pdf
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http://cogprints.org/3088/1/V1OUK1FN.pdf
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V1OUK1FN.pdf

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Technology of data visualization and data modeling is suggested. The basic of the technology is original idea of elastic net and methods of its construction and application. A short review of relevant methods has been made. The methods proposed are illustrated by applying them to the real economical, sociological and biological datasets and to some model data distributions.
The basic of the technology is original idea of elastic net  regular point approximation of some manifold that is put into the multidimensional space and has in a certain sense minimal energy. This manifold is an analogue of principal surface and serves as nonlinear screen on what multidimensional data are projected.
Remarkable feature of the technology is its ability to work with and to fill gaps in data tables. Gaps are unknown or unreliable values of some features. It gives a possibility to predict plausibly values of unknown features by values of other ones. So it provides technology of constructing different prognosis systems and nonlinear regressions.
The technology can be used by specialists in different fields. There are several examples of applying the method presented in the end of this paper.
 http://www.ihes.fr/~zinovyev/papers/M0136.pdf
 http://agorban.fatal.ru/neuroinf/pdf/elmap.pdf

Gorban
Alexander. N.
Prof.

Zinovyev
Andrei Yu.
Dr.
Visualization, Data mining, Neural networks,
Selforganizing maps, Genomic
Free software for data visualization is available online: http://www.ihes.fr/~zinovyev/
Contact information:
E.mail: agorban@mat.ethz.ch, zinovyev@ihes.fr
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1. Gorban A. N., Zinovyev A.Yu., Pitenko A. A. Visualization of data using method of elastic maps. Informatsionnie technologii, 'Mashinostrornie' Publ., Moscow, 2000. ?6, pp.2635.
2. Gorban A.N., Rossiev A.A. Method of principal curves for data with gaps. Proc. of 12th international conference "Problems of neurocybernetics". RostovNaDonu, Russia, 1999. pp. 198201.
3. Kivimoto K. Topology Preservation in SOM. Proc. of International Conference on Neural NetWorks. Washington, DC, 1996. Vol.1. PP. 294300.
4. Kohonen T. SelfOrganizing Maps. Springer: Berlin  Heidelberg, 1997.
5. Shumsky S. A., Kochkin A.N. SelfOrganizing maps of financial indicators of the 200 biggest Russian companies. Proc. of AllRussia science conference "NeuroInformatics99". Moscow, 1999. Part 3. P. 122127.
6. Zinovyev A. Yu. Visualization of Multidimensional Data. Krasnoyasrk State Technical University Press, 2000. 168 p.
 compscineuralnets
Visualization of Data by Method of Elastic Maps and Its Applications in Genomics, Economics and Sociology
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