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    Using clustering methods to deal with high number of alternatives on Group Decision Making

    Autor: 
    Morente-Molinera, Juan Antonio
    ;
    Ríos Aguilar, Sergio
    ;
    González-Crespo, Rubén
    ;
    Herrera-Viedma, Enrique
    Fecha: 
    2019
    Palabra clave: 
    clustering methods; large-scale Group Decision Making; linguistic modelling; WOS(2)
    Revista / editorial: 
    7th International Conference on Information Technology and Quantitative Management (ITQM)
    Tipo de Ítem: 
    conferenceObject
    URI: 
    https://reunir.unir.net/handle/123456789/10968
    DOI: 
    https://doi.org/10.1016/j.procs.2019.11.290
    Dirección web: 
    https://www.sciencedirect.com/science/article/pii/S1877050919320022?via%3Dihub
    Open Access
    Resumen:
    Novel Group Decision Making methods and Web 2.0 have augmented the quantity of data that experts have to discuss about. Nevertheless, experts are only capable of dealing with a reduced set of information. In this paper, a novel method for dealing with decision environments that include a large set of alternatives is presented. By the use of clustering methods, the available alternatives are combined into clusters according to their similarity. Afterwards, one Group Decision Making process is employed for choosing a cluster and another one for selecting the final alternative. (C) 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-ne-nd/4.0/) Peer-review under responsibility of the scientific committee of the 7th International Conference on Information Technology and Quantitative Management (ITQM 2019)
    Descripción: 
    Ponencia del Congreso "7th International Conference on Information Technology and Quantitative Management (ITQM) - Information Technology and Quantitative Management Based on Artificial Intelligence".
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