Name
Abstract | ||
|---|---|---|
Within a large group of decision makers, varying amounts of both conflicting and harmonious views will be prevalent within the group, but obscured due to group size. When the number of Decision Makers is large, utilizing clustering during the process of aggregation of their views should aid both knowledge discovery - about the group's conflict and consensus - as well as helping to streamline the aggregation process to reach a group consensus. We conjecture that this can be realized by using the similarity of views of a large group of decision makers to define clusters of analogous opinions. From each cluster of decision makers, a representation of the views of its members can then be sought. This set of representations can then be utilized for aggregation to help reach a final whole group consensus. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/MCDM.2014.7007186 | Computational Intelligence in Multi-Criteria Decision-Making |
Keywords | Field | DocType |
decision making,pattern clustering,pattern matching,aggregation process,decision maker clustering,knowledge discovery,member views representation,Clustering,Genetic algorithms,Inconsistency,Multi-criteria decision making,Multi-objective optimization,Pairwise comparison | Pairwise comparison,Computer science,Multi-objective optimization,Artificial intelligence,Knowledge extraction,Cluster analysis,Conjecture,Machine learning,Genetic algorithm | Conference |
Citations | PageRank | References |
2 | 0.36 | 7 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Edward Abel | 1 | 24 | 4.85 |
| Ludmil Mikhailov | 2 | 215 | 14.89 |
| John A. Keane | 3 | 695 | 92.81 |