A New Clustering Algorithm With Preference Adjustment Cost to Reduce the Cooperation Complexity in Large-Scale Group Decision Making

In large-scale group decision making (LSGDM), appropriate clustering analysis is important to consensus reaching since it can reduce the interactive complexity among individuals. According to the traditional clustering method, a conflict may arise between the consensus reaching levels and total adju...

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Bibliographic Details
Published in:IEEE transactions on systems, man, and cybernetics. Systems Vol. 52; no. 8; pp. 5271 - 5283
Main Authors: Wu, Tong, Liu, Xinwang, Qin, Jindong, Herrera, Francisco
Format: Journal Article
Language:English
Published: New York IEEE 01.08.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Adjustment cost
Algorithms
Cluster analysis
Clustering
Clustering algorithms
clustering analysis
Complexity
Complexity theory
consensus reaching process
Costs
Decision analysis
Decision making
Extraterrestrial measurements
large-scale group decision making (LSGDM)
Linguistics
Preferences
Proposals
ISSN:2168-2216, 2168-2232
Online Access:Get full text
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Summary:In large-scale group decision making (LSGDM), appropriate clustering analysis is important to consensus reaching since it can reduce the interactive complexity among individuals. According to the traditional clustering method, a conflict may arise between the consensus reaching levels and total adjustment costs within clusters when individuals have different unit adjustment cost, which reflects their willingness to make concessions. Since this conflict may aggravate the consensus complexity, we propose a new <inline-formula> <tex-math notation="LaTeX">K </tex-math></inline-formula>-means clustering method that considers both preferences and the preference adjustment cost. The preference adjustment cost is attached to preferences with a parameter that can be determined by balancing this conflict. Because of such conflict, the proposed clustering algorithm can improve the similarity of intracluster individuals on the preference adjustment cost by offsetting some acceptable consensus reaching levels within clusters. According to the proposed clustering algorithm, individuals who have both similar preferences and adjustment willingness are classified into the same clusters. In this way, the moderator can provide similar compensation strategies for intracluster individuals, which will decrease the adjustment complexity. A practical case study of team construction examines the application of the proposed algorithm, and the related comparative analysis shows that it is convenient for managers to persuade individuals to reach a consensus under the improved clustering results.
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ISSN:2168-2216
2168-2232
DOI:10.1109/TSMC.2021.3120809