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UDC 658.1-50 DOI: 10.14529/ctcr150217 Some Generalization of Combined Method «Factor Analyze + Taxonomy» V.D. Mazurov, Ural Federal University named after the First President of Russia Boris Yeltsin, Ekaterinburg, Russian Federation, vldmazurov@gmail.comAbstractThe article discusses the generalized method of analysis of observational data needed to detect regularities assess the importance of attributes of the objects and finding the hidden factors. The proposed method is based on the use of discriminated analysis, taxonomy and evaluation of the information subsystems signs. Analysis of the data suggests not only the search for the underlying factors, but also the discovery of hidden patterns. A model of the problem of detection of regularities is suggested, that is its reduction to the discriminanted analysis – the problem of separating sets. In this case, it was sufficient to consider the case of the partition into two classes. In the absence of an analytical description of this partition, work with objects, you can only interact with their devices and examinations. Then the classes recovery makes by the relevant case sets. Specific applications of the results are the search problem patterns in non-formalized problems of mathematical economics, mathematical biology and medicine. The article shows that the theory of algorithms allows us to analyze not only formalized tasks, but also, in principle, non-formalizable. Keywordsdiscriminant analysis, taksonomy, structure of the hypergraph, the theory of algorithms References1. Zagoruyko N.G. Prikladnye metody analiza dannykh i znaniy [Applied Methods of the Analysis of Data and Knowledge]. Novosibirsk, Institute of Mathematics, 1999, 267 p. 2. Bravermann E.M., Muchnik I.B. Strukturnye metody obrabotki empiricheskikh dannykh [Structural Methods of Processing Empirical Data]. Moscow, Science, 1983, 404 p. 3. Mazurov V.D., Potanin N.I. Neural Networks and Examinations. Tezisy konferentsii IMM Uro RAN [Theses of the IMM Conference Uro RAN], 1983. 4. Vapnik V.N. Teoriya raspoznavaniya obrazov [Theory of Recognition of Images]. Moscow, Science, 1974б 416 р. 5. Mazurov V.D., Eremin I.I. [Organize Chaos]. News of USU. Ser. Social Sciences, 2001, No. 21, pр. 6–9. (in Russ.) 6. Donskoy V.I. [Synthesis of the Coordinated Linear Optimizing Models According to Case Information]. Scientific Notes of TNU. Ser. Physical and Math Sciences, 2010, vol. 23, no. 2, pp. 56–65. (in Russ.) 7. Kalyadin N.I. Konstruktivizatsiya modeley klassifikatsii konechnykh ob”ektov [The Construсtivization of the Classification Model Targets]. Izhevsk, Bulletin of the Institute of Mathematics and Informative of Udmurt State University, 2014, no. 1(38), 231 p. 8. Mazurov Vl. D. Metod komitetov v zadachakh optimizatsii i klassifikatsii [Metod of Committees in Problems of Optimization and Classifications]. Moscow, Science , 1990, 248 p. 9. Mazurov Vl., Gilev D.V. Model of Dynamics of Objects in Contradictory Conditions. Sworld. Problems and Ways of their Solution in Science, Transport and so on. December, 2012, pp. 34–41. 10. Khachay M. Yu. Komitetnye resheniya nesovmestnykh system ogranicheniy i metody obucheniya raspoznavaniyu. Avtoref. doct. dis. [Komitetnye Decisions of Not Joint Systems of Restrictions and Methods of Training in Recognition. Abstract of doct. diss.]. Ekaterinburg, 2004. 175 p. SourceBulletin of the South Ural State University. Ser. Computer Technologies, Automatic Control, Radio Electronics, 2015, vol. 15, no. 2, pp. 139-142. (in Russ.) (Brief Reports) |