Advertisement

A Generalization of Symbolic Data Analysis Allowing the Processing of Fuzzy Granules

  • Vasile Georgescu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3131)

Abstract

The symbolic data analysis is a new trend in multivariate descriptive statistics whose main purpose consists in analyzing and processing set-valued random variables. Such variables are derived by summarizing large datasets and abstracting information in aggregated form. Some typical examples of symbolic datasets are those encoded by means of interval-valued variables or modal variables. Unlike classical data, symbolic data can be structured and can contain internal variation. The aim of this paper is to extend the formal framework of symbolic data analysis for allowing fuzzy-valued variables to deal with. Some related approaches based on granular computing are also proposed or discussed.

Keywords

Information Granule Granular Computing Fuzzy Event Fuzzy Distance Symbolic Data Analysis 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Cazes, P., Chouakria, A., Diday, E., Schecktman, Y.: Extension de l’Analyse en Composantes Principales à des données intervalles. Revue de Statistiques Appliquées XXXVIII(3) (1997)Google Scholar
  2. 2.
    Diday, E.: L’Analyse des données symboliques: un cadre théorique et des outils. Cahiers du CEREMADE. N° 9821 (1998)Google Scholar
  3. 3.
    Georgescu, V.: Reconstructing configurations of fuzzy granules by a non-conventional multidimensional scaling method.In: Proceedings of ICMS 2004-Spain (2004) (forthcoming) Google Scholar
  4. 4.
    Georgescu, V.: A fuzzy generalization of principal component analysis and hierarchical clustering.In: Proceedings of the Third Congress of SIGEF, Buenos Aires, Paper 2.25 (1996) Google Scholar
  5. 5.
    Georgescu, V.: Multivariate fuzzy-termed data analysis: issues and methods. Fuzzy Economic Review VI(1), 19–48 (2001)Google Scholar
  6. 6.
    Georgescu, V.: On the foundations of granular computing paradigm.  VIII(2), 73–105 (2003)Google Scholar
  7. 7.
    Pedrycz, W., Bargiela, A.: Granular clustering: a granular signature of data. IIEEE Transactions on Systems, Man and Cybernetics 32(2), 212–224 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Vasile Georgescu
    • 1
  1. 1.Faculty of EconomicsUniversity of CraiovaCraiovaRomania

Personalised recommendations