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A cohesion measure for improving the weighting of experts’ subgroups in large-scale group decision making clustering methods

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Abstract
Nowadays, due to the societal and technological trends such as big data, social networks or e-democracy, large-scale group decision making has become one of the most relevant research topics within the decision making area. The participation of a large number of experts in the decision process, implies several new challenges in the resolution of these problems, such as scalability, opinion polarization, etc. Our interest in this contribution is focused on scalability of this type of problems, in which clustering methods have been used to manage the scalability by grouping experts into several subgroups with similar opinions. The importance assigned to each subgroup is a pivotal issue, since it has direct influence in the final solution. Until now, such importance has been assigned according to the size of the subgroup, discarding the togetherness degree among their preferences, which might lead to a missassignment of the weights of the subgroup and, in the end, an unfair solution of the problem. This contribution presents a novel process to compute the weights assigned to the experts’ subgroups according to their cohesion, with the aim of assigning properly their importance and obtain fair solutions for large-scale group decision making problems
Keywords
large-scale group decision making, fuzzy preference relation, clustering model, cohesion measure, Decision making, Clustering methods, Proposals, Scalability, Clustering algorithms, Weight measurement, Computer science

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MLA
Labella, Alvaro, et al. “A Cohesion Measure for Improving the Weighting of Experts’ Subgroups in Large-Scale Group Decision Making Clustering Methods.” 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE, 2019.
APA
Labella, A., Rodriguez, R. M., De Tré, G., & Martinez, L. (2019). A cohesion measure for improving the weighting of experts’ subgroups in large-scale group decision making clustering methods. In 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). New Orleans, USA: IEEE.
Chicago author-date
Labella, Alvaro, Rosa M. Rodriguez, Guy De Tré, and Luis Martinez. 2019. “A Cohesion Measure for Improving the Weighting of Experts’ Subgroups in Large-Scale Group Decision Making Clustering Methods.” In 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE.
Chicago author-date (all authors)
Labella, Alvaro, Rosa M. Rodriguez, Guy De Tré, and Luis Martinez. 2019. “A Cohesion Measure for Improving the Weighting of Experts’ Subgroups in Large-Scale Group Decision Making Clustering Methods.” In 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE.
Vancouver
1.
Labella A, Rodriguez RM, De Tré G, Martinez L. A cohesion measure for improving the weighting of experts’ subgroups in large-scale group decision making clustering methods. In: 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE; 2019.
IEEE
[1]
A. Labella, R. M. Rodriguez, G. De Tré, and L. Martinez, “A cohesion measure for improving the weighting of experts’ subgroups in large-scale group decision making clustering methods,” in 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), New Orleans, USA, 2019.
@inproceedings{8627011,
  abstract     = {Nowadays, due to the societal and technological trends such as big data, social networks or e-democracy, large-scale group decision making has become one of the most relevant research topics within the decision making area. The participation of a large number of experts in the decision process, implies several new challenges in the resolution of these problems, such as scalability, opinion polarization, etc. Our interest in this contribution is focused on scalability of this type of problems, in which clustering methods have been used to manage the scalability by grouping experts into several subgroups with similar opinions. The importance assigned to each subgroup is a pivotal issue, since it has direct influence in the final solution. Until now, such importance has been assigned according to the size of the subgroup, discarding the togetherness degree among their preferences, which might lead to a missassignment of the weights of the subgroup and, in the end, an unfair solution of the problem. This contribution presents a novel process to compute the weights assigned to the experts’ subgroups according to their cohesion, with the aim of assigning properly their importance and obtain fair solutions for large-scale group decision making problems},
  author       = {Labella, Alvaro and Rodriguez, Rosa M. and De Tré, Guy and Martinez, Luis},
  booktitle    = {2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)},
  isbn         = {9781538617281},
  issn         = {1558-4739},
  keywords     = {large-scale group decision making,fuzzy preference relation,clustering model,cohesion measure,Decision making,Clustering methods,Proposals,Scalability,Clustering algorithms,Weight measurement,Computer science},
  language     = {eng},
  location     = {New Orleans, USA},
  pages        = {6},
  publisher    = {IEEE},
  title        = {A cohesion measure for improving the weighting of experts’ subgroups in large-scale group decision making clustering methods},
  url          = {http://dx.doi.org/10.1109/FUZZ-IEEE.2019.8858858},
  year         = {2019},
}

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