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...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	IEEE transactions on systems, man, and cybernetics. Systems Ročník 52; číslo 8; s. 5271 - 5283
Hlavní autori:	Wu, Tong, Liu, Xinwang, Qin, Jindong, Herrera, Francisco
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.08.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Buďte prvý, kto okomentuje tento záznam!