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dc.contributor.authorMorente-Molinera, Juan Antonio
dc.contributor.authorRíos Aguilar, Sergio
dc.contributor.authorGonzález-Crespo, Rubén
dc.contributor.authorHerrera-Viedma, Enrique
dc.date2019
dc.date.accessioned2021-02-03T15:22:42Z
dc.date.available2021-02-03T15:22:42Z
dc.identifier.issn1877-0509
dc.identifier.urihttps://reunir.unir.net/handle/123456789/10968
dc.descriptionPonencia del Congreso "7th International Conference on Information Technology and Quantitative Management (ITQM) - Information Technology and Quantitative Management Based on Artificial Intelligence".es_ES
dc.description.abstractNovel 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)es_ES
dc.language.isoenges_ES
dc.publisher7th International Conference on Information Technology and Quantitative Management (ITQM)es_ES
dc.relation.ispartofseries;vol. 162
dc.relation.urihttps://www.sciencedirect.com/science/article/pii/S1877050919320022?via%3Dihubes_ES
dc.rightsopenAccesses_ES
dc.subjectclustering methodses_ES
dc.subjectlarge-scale Group Decision Makinges_ES
dc.subjectlinguistic modellinges_ES
dc.subjectWOS(2)es_ES
dc.titleUsing clustering methods to deal with high number of alternatives on Group Decision Makinges_ES
dc.typeconferenceObjectes_ES
reunir.tag~ARIes_ES
dc.identifier.doihttps://doi.org/10.1016/j.procs.2019.11.290


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