Aggregation of fuzzy relations and preservation of transitivity

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Abstract

This contribution provides a comprehensive overview on the theoretical framework of aggregating fuzzy relations under the premise of preserving underlying transitivity conditions. As such it discusses the related property of dominance of aggregation operators. After a thorough introduction of all necessary and basic properties of aggregation operators, in particular dominance, the close relationship between ag$gregating fuzzy relations and dominance is shown. Further, principles of building dominating aggregation operators as well as classes of aggregation operators dominating one of the basic t-norms are addressed. In the paper by Bodenhofer, Küng and Saminger, also in this volume, the interested reader finds an elaborated (real world) example, i.e., an application of the herein contained theoretical framework.
Original languageEnglish
Title of host publicationTheory and Applications of Relational Structures as Knowledge Instruments II - International Workshops of COST Action 274, TARSKI, 2002-2005, Selected Revised Papers
Editors H. C. M. de Swart, E. Orlowska, M. Roubens, G. Schmidt
Place of PublicationBerlin Heidelberg
PublisherSpringer Verlag
Pages185-206
Number of pages22
ISBN (Print)3540692231, 9783540692232
DOIs
Publication statusPublished - 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4342 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Fields of science

  • 101 Mathematics
  • 101004 Biomathematics
  • 101027 Dynamical systems
  • 101013 Mathematical logic
  • 101028 Mathematical modelling
  • 101014 Numerical mathematics
  • 101020 Technical mathematics
  • 101024 Probability theory
  • 102001 Artificial intelligence
  • 102003 Image processing
  • 102009 Computer simulation
  • 102019 Machine learning
  • 102023 Supercomputing
  • 202027 Mechatronics
  • 206001 Biomedical engineering
  • 206003 Medical physics
  • 102035 Data science

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