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)


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.


Information Granule Granular Computing Fuzzy Event Fuzzy Distance Symbolic Data Analysis 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

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

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