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.
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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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García-Lapresta, J.L., Pérez-Román, D. (2017). Consensus-Based Clustering in Numerical Decision-Making. In: Ferraro, M., 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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