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

Juan Antonio Morente-Molinera | Zendy; Sergio Ríos Aguilar | Zendy; Rubén González Crespo | Zendy; Enrique HerreraViedma | Zendy

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

Author(s) -

Juan Antonio Morente-Molinera,

Sergio Ríos Aguilar,

Rubén González Crespo,

Enrique HerreraViedma

Publication year - 2019

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2019.11.290

Subject(s) - computer science , cluster analysis , set (abstract data type) , similarity (geometry) , data mining , group decision making , group (periodic table) , process (computing) , cluster (spacecraft) , information retrieval , machine learning , artificial intelligence , chemistry , organic chemistry , political science , law , image (mathematics) , programming language , operating system

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.

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