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International Conference on Intelligent Data Engineering and Automated Learning

IDEAL 2006: Intelligent Data Engineering and Automated Learning – IDEAL 2006 pp 16–24Cite as

On Correlation Measures of Intuitionistic Fuzzy Sets

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 4224))

Abstract

The intuitionistic fuzzy set, developed by Atanassov [1], is a useful tool to deal with vagueness and uncertainty.Correlation analysis of intuitionistic fuzzy sets is an important research topic in the intuitionistic fuzzy set theory and has great practical potential in a variety of areas, such as engineering, decision making, medical diagnosis, pattern recognition, etc. In this paper, we propose a new method for deriving the correlation coefficients of intuitionistic fuzzy sets, which has some advantages over the existing methods. Furthermore, we extend the developed method to the interval-valued intuitionistic fuzzy set theory, and show its application in medical diagnosis.

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Author information

Authors and Affiliations

  1. Department of Management Science and Engineering, School of Economics and Management, Tsinghua University, Beijing, 100084, China

    Zeshui Xu

Authors
  1. Zeshui Xu
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Editor information

Editors and Affiliations

  1. Escuela Politécnica Superior, GICAP Research Group, Universidad de Burgo, Calle Francisco de Vitoria S/N, Edifico C, Campus Vena, 09006, Burgos, Spain

    Emilio Corchado

  2. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

  3. Department of Information Systems and Computation, Technical University of Valencia, Camino de Vera, Valencia, Spain

    Vicente Botti

  4. University of West Scotland, PA1 2BE, Paisley, Scotland

    Colin Fyfe

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© 2006 Springer-Verlag Berlin Heidelberg

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Xu, Z. (2006). On Correlation Measures of Intuitionistic Fuzzy Sets. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2006. IDEAL 2006. Lecture Notes in Computer Science, vol 4224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875581_2

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  • DOI: https://doi.org/10.1007/11875581_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-45485-4

  • Online ISBN: 978-3-540-45487-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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