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The Methodology of Descriptive Analysis of Multidimensional Data Based on Combining of Intelligent Technologies

  • T. AfanasievaEmail author
  • A. Shutov
  • E. Efremova
  • E. Bekhtina
Conference paper
  • 8 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1156)

Abstract

There are many intelligent technologies successfully used for descriptive analysis of multidimensional numerical data. The paper focuses on developing the methodology for complex descriptive analysis of such data by their multi-level granulation in groups meaningful for domain experts. For this goal the methodology to combine following intelligent technologies: clustering of numeric data, formal concept analysis, fuzzy scales and linguistic summarizing is proposed. The proposed methodology of analysis is useful for extraction of properties from multidimensional numerical data, starting with the formation of groups of objects similar in quantitative terms, and ending with their linguistic interpretation by propositions included qualitative properties. Basic definitions, problem statement, step by step representing of methodology for complex descriptive analysis of multidimensional numerical data and case study are provided.

Keywords

Descriptive analysis Multidimensional numerical data Intelligent technologies Granulating Formal concept analysis Linguistic summarizing 

References

  1. 1.
    Kocherlakota, S.M., Healey Ch. G.: Interactive Visual Summarization of Multidimensional Data. In: Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics (SMC 2009), pp. 362–369, San Antonio, TX, USA (2009)Google Scholar
  2. 2.
    Chakraborty, T.: Combining clustering and classification for ensemble learning. J. Latex Class Files 13(9), 1–14 (2014)Google Scholar
  3. 3.
    Bini, B.S., Mathew, T.: Clustering and regression techniques for stock prediction. Procedia Technol. 24, 1248–1255 (2016)CrossRefGoogle Scholar
  4. 4.
    Deshpande, A.R., Lobo, L.M.R.J.: Text summarization using clustering technique. Int. J. Eng. Trends Technol. (IJETT) 4(8), 3348–3351 (2013)Google Scholar
  5. 5.
    Kacprzyk, J., Wilbik, A., Zadrożny, S.: Linguistic summarization of time series under different granulation of describing features. In: Kryszkiewicz, M., Peters, J.F., Rybinski, H., Skowron, A. (eds) Rough Sets and Intelligent Systems Paradigms. RSEISP 2007, LNCS. vol. 4585, Springer, Heidelberg (2007)Google Scholar
  6. 6.
    Boran, E., Akay, D., Yager, R.R.: An overview of methods for linguistic summarization with fuzzy sets. Expert Syst. Appl. 61(C), 129–144 (2016)Google Scholar
  7. 7.
    Ganter, B., Kuznetsov, S.O.: Pattern structures and their projections. In: Conceptual Structures: Broadening the Base, Lecture Notes in Computer Science, vol. 2120, pp. 129–142. Springer, Heidelberg (2001)Google Scholar
  8. 8.
    Nersisyan, S., Pankratieva, V., Staroverov, V., Podolskii, V.: A. greedy clustering algorithm based on interval pattern concepts and the problem of optimal box positioning. J. Appl. Math. 2017, 1–9 (2017). Article ID 4323590Google Scholar
  9. 9.
    Yager, R.R., Ford, K.M., Cañas, A.J.: An approach to the linguistic summarization of data. information processing and management of uncertainty in knowledge based systems. In: An Approach to the Linguistic Summarization of Data. Springer-Verlag, Berlin (1991)Google Scholar
  10. 10.
    Zadeh, L.A.: A prototype-centered approach to adding deduction capabilities to search engines – the concept of a protoform. In: Proceedings of the Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS 2002), pp. 523–525 (2002)Google Scholar
  11. 11.
    Kacprzyk, J., Zadrozny, S.: Linguistic summaries of time series: a powerful tool for discovering knowledge on time varying processes and systems. Informatyka Stosowana 1, 149–160 (2014)Google Scholar
  12. 12.
    Ganter, B., Wille, R.: Formal Concept Analysis: Mathematical Foundations. Springer Verlag, Berlin (1999)CrossRefGoogle Scholar
  13. 13.
    Gugisch, R.: Many-valued context analysis using descriptions. In: 9th International Conference on Conceptual Structures, pp. 157–168, Stanford, CA, USA (2001)Google Scholar
  14. 14.
    Afanasieva, T., Yarushkina, N., Gyskov, G.: ACL-scale as a tool for preprocessing of many-valued. In: The Second International Workshop on Soft Computing Applications and Knowledge Discovery, pp. 2–11 (2016)Google Scholar
  15. 15.
    Charrad, M., Ghazzali, N., Boiteau, V., Niknafs, A.: NbClust: an R package for determining the relevant number of clusters in a data set. J. Stat. Softw. 61(6), 1–36 (2014)CrossRefGoogle Scholar
  16. 16.
    Kardiovaskulyarnaya profilaktika. Natsional’nyye rekomendatsii. Razrabotany komitetom ekspertov rossiyskogo obshchestva kardiologov. Kardiovaskulyarnaya terapiya i profilaktika, 10(6) (2011)Google Scholar
  17. 17.
    Piepoli, M.F., Hoes, A.W., Agewall, S., Albus, C., Brotons, C., Catapano, A.L., Cooney, M.T., Corrà, U., Cosyns, B., Deaton, C., Graham, I., Hall, M.S., Hobbs, F.D.R., Løchen, M.L., Löllgen, H., Marques-Vidal, P., Perk, J., Prescott, E., Redon, J., Richter, D.J., Sattar, N., Smulders, Y., Tiberi, M., van der Worp, H.B., van Dis, I., Verschuren, W.M.M., Binno, S.: European guidelines on cardiovascular disease prevention in clinical practice. Eur. Heart J. 37, 2315–2381 (2016)CrossRefGoogle Scholar
  18. 18.
    Arnett, D.K., Blumenthal, R.S., Albert, M.A., Buroker, A.B., Goldberger, Z.D., Hahn, E.J., Himmelfarb, C.D., Khera, A., Lloyd-Jones, D., McEvoy, J.W., Michos, E.D., Miedema, M.D., Muñoz, D., Smith Jr., S.C., Virani, S.S., Williams Sr., K.A., Yeboah, J., Ziaeian, B.: ACC/AHA guideline on the primary prevention of cardiovascular disease. J. Am. Coll. Cardiol. 73(12), 1494–1563 (2019)CrossRefGoogle Scholar
  19. 19.
    Levin, A., Stevens, P.E., Bilous, R.W., Coresh, J., De Fran-cisco, A.L.M., De Jong, P.E., Griffith, K.E., Hemmelgarn, B.R., Iseki, K., Lamb, E.J., Levey, A.S., Riella, M.C., Shlipak, M.G., Wang, H., White, C.T., Winearls, C.G.: Kidney disease: improving global outcomes (KDIGO) CKD work group. KDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease. Kidney Int. Suppl. 3, 1–150 (2013)Google Scholar
  20. 20.
    Sheilini, M., Hande, H.M., Prabhu, M.M., Pai, M.S., George, A.: Impact of multimodal interventions on medication nonadherence among elderly hypertensives: a randomized controlled study. Patient Prefer Adherence 13, 549–559 (2019)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Ulyanovsk State Technical UniversityUlyanovskRussia
  2. 2.Ulyanovsk State UniversityUlyanovskRussia

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