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

Abstract

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.