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Consensus-Based Agglomerative Hierarchical Clustering

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Fuzzy Sets, Rough Sets, Multisets and Clustering

Part of the book series: Studies in Computational Intelligence ((SCI,volume 671))

Abstract

In this contribution, 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.

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Acknowledgements

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).

Author information

Authors and Affiliations

  1. PRESAD Research Group, BORDA Research Unit, IMUVA, Departamento de Economía Aplicada, Universidad de Valladolid, Valladolid, Spain

    José Luis García-Lapresta

  2. PRESAD Research Group, BORDA Research Unit, Departamento de Organización de Empresas y C.I.M., Universidad de Valladolid, Valladolid, Spain

    David Pérez-Román

Authors
  1. José Luis García-Lapresta
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  2. David Pérez-Román
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Corresponding author

Correspondence to José Luis García-Lapresta .

Editor information

Editors and Affiliations

  1. University of Skövde, School of Informatics University of Skövde, Skövde, Sweden

    Vicenç Torra

  2. University of Skövde, School of Informatics University of Skövde, Skövde, Sweden

    Anders Dahlbom

  3. Toho Gakuen , Tokyo, Japan

    Yasuo Narukawa

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García-Lapresta, J.L., Pérez-Román, D. (2017). Consensus-Based Agglomerative Hierarchical Clustering. In: Torra, V., Dahlbom, A., Narukawa, Y. (eds) Fuzzy Sets, Rough Sets, Multisets and Clustering. Studies in Computational Intelligence, vol 671. Springer, Cham. https://doi.org/10.1007/978-3-319-47557-8_8

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  • DOI: https://doi.org/10.1007/978-3-319-47557-8_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47556-1

  • Online ISBN: 978-3-319-47557-8

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