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.
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Georgescu, V. (2004). A Generalization of Symbolic Data Analysis Allowing the Processing of Fuzzy Granules. In: Torra, V., Narukawa, Y. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2004. Lecture Notes in Computer Science(), vol 3131. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-27774-3_21
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DOI: https://doi.org/10.1007/978-3-540-27774-3_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22555-3
Online ISBN: 978-3-540-27774-3
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