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Fuzzy Weighted Attribute Combinations Based Similarity Measures

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Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10369))

Abstract

Some similarity measures for fuzzy subsets are introduced: they are based on fuzzy set-theoretic operations and on a weight capacity expressing the degree of contribution of each group of attributes. For such measures, the properties of dominance and T-transitivity are investigated.

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Acknowledgment

This work was partially supported by INdAM-GNAMPA through the Project 2015 U2015/000418 and the Project 2016 U2016/000391 and by the Italian Ministry of Education, University and Research, under grant 2010FP79LR_003.

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Authors and Affiliations

  1. Dip. Matematica e Informatica, Università di Perugia, Perugia, Italy

    Giulianella Coletti

  2. Dip. Economia, Università di Perugia, Perugia, Italy

    Davide Petturiti

  3. Dip. S.B.A.I., Università di Roma “La Sapienza”, Rome, Italy

    Barbara Vantaggi

Authors
  1. Giulianella Coletti
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  2. Davide Petturiti
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  3. Barbara Vantaggi
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Corresponding author

Correspondence to Barbara Vantaggi .

Editor information

Editors and Affiliations

  1. IDSIA, Lugano, Switzerland

    Alessandro Antonucci

  2. ONERA, Toulouse, France

    Laurence Cholvy

  3. Aix-Marseille University, Marseille, France

    Odile Papini

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Coletti, G., Petturiti, D., Vantaggi, B. (2017). Fuzzy Weighted Attribute Combinations Based Similarity Measures. In: Antonucci, A., Cholvy, L., Papini, O. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2017. Lecture Notes in Computer Science(), vol 10369. Springer, Cham. https://doi.org/10.1007/978-3-319-61581-3_33

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  • DOI: https://doi.org/10.1007/978-3-319-61581-3_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61580-6

  • Online ISBN: 978-3-319-61581-3

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