Clustering Alternatives and Learning Preferences Based on Decision Attitudes and Weighted Overlap Dominance

  • Camilo Franco
  • Jens Leth Hougaard
  • Kurt Nielsen
Part of the Studies in Computational Intelligence book series (SCI, volume 671)


An initial assessment on a given set of alternatives is necessary for understanding complex decision problems and their possible solutions. Attitudes and preferences articulate and come together under a decision process that should be explicitly modeled for understanding and solving the inherent conflict of decision making. This paper revises multi-criteria modeling of imprecise data, inferring outranking and indifference binary relations and classifying alternatives according to their similarity or dependency. After the initial assessment on the set of alternatives, preference orders are built according to the attitudes of decision makers, aiding the decision process by identifying solutions with minimal dissention.


Decision attitudes Dependency-based clustering Preference learning Consensus and dissention 



This research has been partially supported by the Danish Industry Foundation and the Center for research in the Foundations of Electronic Markets (CFEM), funded by the Danish Council for Strategic Research.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Camilo Franco
    • 1
  • Jens Leth Hougaard
    • 1
  • Kurt Nielsen
    • 1
  1. 1.IFRO, Faculty of ScienceCopenhagen UniversityFrederiksbergDenmark

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