Managing Situations with High Number of Elements in Group Decision Making

Group Decision Making environments have completely changed. The number of information that the experts have available and that, therefore, they can use to discuss about is constantly increasing. There is a need of new Group Decision Making methods, like the one developed in this paper, that are capable of dealing with environments where the number of alternatives is high. In this paper, clustering methods are used in order to sort alternatives in categories and help experts in the task of making a decision.

[1]  Francisco Herrera,et al.  Computing with words in decision making: foundations, trends and prospects , 2009, Fuzzy Optim. Decis. Mak..

[2]  Yejun Xu,et al.  Alternative Ranking-Based Clustering and Reliability Index-Based Consensus Reaching Process for Hesitant Fuzzy Large Scale Group Decision Making , 2019, IEEE Transactions on Fuzzy Systems.

[3]  Konstantin E. Samouylov,et al.  Carrying out consensual Group Decision Making processes under social networks using sentiment analysis over comparative expressions , 2019, Knowl. Based Syst..

[4]  Francisco Herrera,et al.  A Consensus Model for Large-Scale Linguistic Group Decision Making With a Feedback Recommendation Based on Clustered Personalized Individual Semantics and Opposing Consensus Groups , 2019, IEEE Transactions on Fuzzy Systems.

[5]  Shui Yu,et al.  Consensus efficiency in group decision making: A comprehensive comparative study and its optimal design , 2019, Eur. J. Oper. Res..

[6]  Witold Pedrycz,et al.  A method based on PSO and granular computing of linguistic information to solve group decision making problems defined in heterogeneous contexts , 2013, Eur. J. Oper. Res..

[7]  Enrique Herrera-Viedma,et al.  Multiple Attribute Strategic Weight Manipulation With Minimum Cost in a Group Decision Making Context With Interval Attribute Weights Information , 2019, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[8]  G. De Soete,et al.  Clustering and Classification , 2019, Data-Driven Science and Engineering.

[9]  Francisco Chiclana,et al.  Geo-uninorm consistency control module for preference similarity network hierarchical clustering based consensus model , 2018, Knowl. Based Syst..

[10]  Bruce Fischl,et al.  AnatomiCuts: Hierarchical clustering of tractography streamlines based on anatomical similarity , 2016, NeuroImage.

[11]  Enrique Herrera-Viedma,et al.  A Mobile Decision Support System for Dynamic Group Decision-Making Problems , 2010, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[12]  Enrique Herrera-Viedma,et al.  Decision Support System for Decision Making in Changeable and Multi-Granular Fuzzy Linguistic Contexts , 2016, J. Multiple Valued Log. Soft Comput..

[13]  B. Arranz,et al.  Classification of patients with bipolar disorder using k-means clustering , 2019, PloS one.