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Consensus-Based Clustering in Numerical Decision-Making

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Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 456)

Abstract

In this paper, we consider that a set of agents assess a set of alternatives through numbers in the unit interval. In this setting, we introduce a measure that assigns a degree of consensus to each subset of agents with respect to every subset of alternatives. This consensus measure is defined as 1 minus the outcome generated by a symmetric aggregation function to the distances between the corresponding individual assessments. We establish some properties of the consensus measure, some of them depending on the used aggregation function. We also introduce an agglomerative hierarchical clustering procedure that is generated by similarity functions based on the previous consensus measures.

Keywords

  • Linear Order
  • Unit Interval
  • Gini Index
  • Aggregation Function
  • Weak Order

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Acknowledgments

The authors gratefully acknowledge the funding support of the Spanish Ministerio de Economía y Competitividad (project ECO2012-32178) and Consejería de Educación de la Junta de Castilla y León (project VA066U13).

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Correspondence to José Luis García-Lapresta .

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García-Lapresta, J.L., Pérez-Román, D. (2017). Consensus-Based Clustering in Numerical Decision-Making. In: , et al. Soft Methods for Data Science. SMPS 2016. Advances in Intelligent Systems and Computing, vol 456. Springer, Cham. https://doi.org/10.1007/978-3-319-42972-4_30

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  • DOI: https://doi.org/10.1007/978-3-319-42972-4_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42971-7

  • Online ISBN: 978-3-319-42972-4

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