Automation and Remote Control

, Volume 62, Issue 3, pp 467–473 | Cite as

Restoration of Spaces in Data by the Method of Nonhierarchical Decompositions

  • S. D. Dvoenko


The method of nonhierarchical decompositions was considered as an extension of the nonhierarchical divisive methods of clustering and grouping. It can be used for constructing simple algorithms to restore the data spaces. Relationship of this method with some existing algorithms to restore the data spaces was demonstrated.


Mechanical Engineer System Theory Simple Algorithm Data Space Divisive Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Dvoenko, S.D., Nonhierarchical Divisive Clustering Algorithm, Avtom. Telemekh., 1999, no. 4, pp. 117–124.Google Scholar
  2. 2.
    Dvoenko, S.D., Nonhierarchical Divisive Grouping Algorithm, Avtom. Telemekh., 1999, no. 9, pp. 47–57.Google Scholar
  3. 3.
    Afifi, A.A. and Azen, S.P., Statistical Analysis, New York: Academic, 1979. Translated under the title Statisticheskii analiz: podkhod s primeneniem EVM, Moscow: Mir, 1982.Google Scholar
  4. 4.
    Elkina, V.N. and Zagoruiko, N.G., ZET Method in Expert Systems, in Analiz raznotipnykh dannykh. Vychislitel'nye sistemy (Analysis of Different-type Data. Computer Systems), Novosibirsk, 1983, issue 99, pp. 73–87.Google Scholar
  5. 5.
    Zagoruiko, N.G., Elkina, V.N., Emel'yanov, S.V., et al., Paket prikladnykh programm OTEKS (OTEKS Package of Application Software), Moscow: Finansy i Statistika, 1986.Google Scholar
  6. 6.
    Zagoruiko, N.G. and Ul'yanov, G.V., Local Methods of Space Filling in Empirical Tables, Ekspertnye sistemy i raspoznavanie obrazov. Vychislitel'nye sistemy (Expert Systems and Pattern Recognition. Computer Systems), Novosibirsk, 1988, no. 126, pp. 75–103.Google Scholar
  7. 7.
    Translated under the title Metody analiza dannykh, Moscow: Finansy i Statistika, 1985.Google Scholar
  8. 8.
    Ai'vazyan, S.A., Bukhshtaber, V.M., Enyukov, I.S., et al., Prikladnaya statistika: Klassifikatsiya i snizhenie razmernosti (Applied Statistics: Classification and Reduction of Dimensionality), Moscow: Finansy i Statistika, 1989.Google Scholar
  9. 9.
    Braverman, E.M. and Muchnik, I.B., Strukturnye metody obrabotki empiricheskikh dannykh (Structural Methods for Processing Empirical Data), Moscow: Nauka, 1983.Google Scholar
  10. 10.
    Duda, R.O. and Hart, P.E., Pattern Classification and Scene Analysis, New York: Wiley, 1973. Translated under the title Raspoznavanie obrazov i analiz stsen, Moscow: Mir, 1976.Google Scholar
  11. 11.
    Duran, B.S. and Odell, P.L., Cluster Analysis. A Survey, Berlin: Springer, 1974. Translated under the title Klasternyi analiz, Moscow: Statistika, 1977.Google Scholar
  12. 12.
    Christofides, N., Graph Theory. An Algorithmic Approach, New York: Academic, 1975. Translated under the title Teoriya grafov. Algoritmicheskii podkhod, Moscow: Mir, 1978.Google Scholar

Copyright information

© MAIK “Nauka/Interperiodica” 2001

Authors and Affiliations

  • S. D. Dvoenko
    • 1
  1. 1.Tula State UniversityTulaRussia

Personalised recommendations