Skip to main content
Log in

Theoretical and Applied Aspects of Automating Multivariate Analysis Procedures

  • General Section
  • Published:
Automatic Documentation and Mathematical Linguistics Aims and scope

Abstract

This paper addresses some important theoretical and applied aspects of modern computer science that are associated with analytical processing of scientific, technical, and economic information. The main trends in using automated non-parametric procedures for logical and mathematical processing of arrays (flows) of digital data are discussed. Some methodological aspects of developing new technological approaches and algorithms for analytical post-processing that allow one to design a wide range of multi-step procedures for assessment and multivariate analysis of scientific, technical, and economic data based on polygram estimation of functionals are considered. It is shown that the procedures and algorithms based on these methods of non-parametric statistics and multivariate data analysis can be useful in various applications, including the development of analytical technologies for big data.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Borisova, L.F. and Syuntyurenko, O.V., VINITI RAN abstract database: Prospects of information postprocessing using methods of data analysis, Sci. Tech. Inf. Process., 2007, vol. 34, no. 6, pp. 278–283.

    Article  Google Scholar 

  2. Syuntyurenko, O.V., Production of information and analytical products and services using the methods of scientometrics and data analysis, Materialy Mezhdun arodnoi konferentsii k 65-letiyu VINITI RAN “Informatsiya v sovremennom mire” (Proc. Int. Conf. on the 65th Anniversary of VINITI RAS Information in the Modern World), Moscow, 2017, pp. 317–321.

    Google Scholar 

  3. Mosteller, F. and Tukey, J.W., Data Analysis and Regression, Pearson, 1977.

    Google Scholar 

  4. Tukey, J.W., Exploratory Data Analysis, Pearson, 1977.

    MATH  Google Scholar 

  5. Kurochkin, E.P., Guaranteed estimation of parameters of processes and systems using limited data, Tekh. Sredstv Svyazi, Ser. TEU, 1986, vol. 2, no. 19, pp. 35–40.

    Google Scholar 

  6. Khampel', F.R., Current trends in the theory of sustainable statistical procedures, Mat. Stat. Prilozh., 1980, vol. 6, pp. 57–59.

    Google Scholar 

  7. Tarasenko, F.P. and Cherepanov, E.V., Polygram evaluation of linear functionals, Mat. Stat. Prilozh., 1985, vol. 12.

  8. Syuntyurenko, O.V., Cherepanov, E.V., and Shchirenko, E.G., Some issues of modeling techno-economic processes and systems based on a multidimensional analysis of factual information on engineering and economics, Materialy IV Vsesoyuznogo simpoziuma “Mashinnye metody obnaruzheniya zakonomernostei” (Proc. IV All-Union Symposium Machine Pattern Recognition Methods), Novosibirsk, 1983, p. 19.

    Google Scholar 

  9. Syuntyurenko, O.V., Simonov, O.V., and Cherepanov, E.V., Some automated procedures for multidimensional analysis of technical and economic data, Tekh. Sredstv Svyazi, Ser. TRPA, 1985, vol. 2, pp. 56–66.

    Google Scholar 

  10. Syuntyurenko, O.V. and Cherepanov, E.V., Computer science: Data analysis and econometrics, Sredstva Svyazi, 1986, no. 4, pp. 39–44.

    Google Scholar 

  11. Iberla, K., Faktornyi analiz (Factor Analysis), Moscow: Statistika, 1980.

    Google Scholar 

  12. Choi, S., Ahn, J.H., and Cichocki, A., Constrained projection approximation algorithms for component analysis, Neural Process. Lett., 2006 vol. 24, pp. 53–65.

    Article  Google Scholar 

  13. Krivenko, M.P., Reconstruction of principal component axes, Inf. Primen., 2018, vol. 12, no. 1, pp. 71–77.

    Google Scholar 

  14. Duren, B.S. and Odell, P.L., Cluster Analysis: A Survey, Springer, 1974.

    Google Scholar 

  15. Zadeh, L.A., Fuzzy sets and their application in pattern recognition and cluster analysis, in Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems, River Edge, NJ: World Scientific Publishing Co., Inc., 1996.

    Chapter  Google Scholar 

  16. Kofman, A., Vvedenie v teoriyu nechetkikh mnozhestv (Introduction to the Theory of Fuzzy Sets), Moscow: Radio i svyaz', 1983.

    Google Scholar 

  17. Lynch, C., Big data: How do your data grow?, Nature, 2008, vol. 455, pp. 28–29.

    Article  Google Scholar 

  18. Rodriguez-Mazahua, L., et al., A general perspective of Big Data: Applications, tools, challenges and trends, J. Supercomput., 2016, vol. 72, pp. 3073–3113.

    Article  Google Scholar 

  19. Tannahill, B.K. and Mo Jamshidi, System of systems and big data analytics–bridging the gap, Comput. Electr. Eng., 2014, vol. 40, no. 1, pp. 2–15.

    Article  Google Scholar 

  20. Weather and mood, Nauka Zhizn, 2014, no. 10, p. 47.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to O. V. Syuntyurenko.

Additional information

Original Russian Text © O.V. Syuntyurenko, 2018, published in Nauchno-Tekhnicheskaya Informatsiya, Seriya 2: Informatsionnye Protsessy i Sistemy, 2018, No. 11, pp. 1–8.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Syuntyurenko, O.V. Theoretical and Applied Aspects of Automating Multivariate Analysis Procedures. Autom. Doc. Math. Linguist. 52, 275–281 (2018). https://doi.org/10.3103/S0005105518060043

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.3103/S0005105518060043

Keywords

Navigation