This book explores clustering operations in the context of social networks and consensus-reaching paths that take into account non-cooperative behaviors. This book focuses on the two key issues in large-scale group decision-making: clustering and consensus building. Clustering aims to reduce the dimension of a large group. Consensus reaching requires that the divergent individual opinions of the decision makers converge to the group opinion.

This book emphasizes the similarity of opinions and social relationships as important measurement attributes of clustering, which makes it different from traditional clustering methods with single attribute to divide the original large group without requiring a combination of the above two attributes. The proposed consensus models focus on the treatment of non-cooperative behaviors in the consensus-reaching process and explores the influence of trust loss on the consensus-reaching process.The logic behind is as follows: firstly, a clustering algorithm is adopted to reduce the dimension of decision-makers, and then, based on the clusters' opinions obtained, a consensus-reaching process is carried out to obtain a decision result acceptable to the majority of decision-makers.

Graduates and researchers in the fields of management science, computer science, information management, engineering technology, etc., who are interested in large-scale group decision-making and consensus building are potential audience of this book. It helps readers to have a deeper and more comprehensive understanding of clustering analysis and consensus building in large-scale group decision-making.

Auteur
Su-Min Yu is Assistant Professor, Distinguished Associate Research Fellow, and Master Supervisor at the College of Management, Shenzhen University. She is currently also a member of the Institute of Big Data Intelligent Management and Decision, Shenzhen University. She received her Ph.D. degree in Management from Central South University, Changsha, China, in 2018. Her research interests include electronic commerce, information management, decision theory and methods, large-scale group decision-making and consensus, big data decision, social network analysis, tourism management, etc. She has presided one project of National Natural Science Foundation. She has published 18 international journal papers in top journals and conference proceedings, including IEEE Transactions on Fuzzy Systems, Information Fusion, Information Sciences, Knowledge-Based Systems, Applied Soft Computing, Computers & Industrial Engineering, Group Decision and Negotiation, International Transactions in Operational Research, among others. Her h-index is 9 with more than 570 citations received in Google Scholar. A total of 4 articles were selected into ESI Global High Citation Paper Database, among which 2 articles were selected as hot papers. She serves as a reviewer in many top-tier international journals in related areas to fuzzy decision-making and group decision-making.
Zhi-Jiao Du is a doctoral candidate at the Business School, Sun Yat-Sen University, China. He is an IEEE student member. He received his B.S. degree in Management from Hainan University, Haikou, China, in 2012, and his M.S. degree in Management from Central South University, Changsha, China, in 2016. He presided over a Special Fund Project of Science and Technology Innovation Cultivation for College Students in Guangdong (Climbing Plan). His research interests include supply chain management, decision theory and methods, large-scale group decision-making and consensus, social network analysis, etc. At present, he has published 14 academic articles in top journals and conference proceedings, including Decision Support Systems, IEEE Transactions on Fuzzy Systems, Information Fusion, Information Sciences, Knowledge-Based Systems, Computers & Industrial Engineering, Group Decision and Negotiation, among others. One of the articles was selected into ESI Global Database of Highly Cited Papers in Computer Science. His h-index is 7 with more than 340 citations received in Google Scholar. He serves as a reviewer in many top-tier international journals in related areas to large-scale group decision-making, supply chain management.

Contenu
Chapter 1. IntroductionChapter 2. Preliminary KnowledgeChapter 3. Trust-Similarity Analysis-Based Clustering MethodChapter 4. Trust-Similarity Measure-Based Hierarchical Clustering MethodChapter 5. Hierarchical Punishment-Driven Consensus Model for Probabilistic Linguistic LSGDMChapter 6. Confidence Consensus-Based Model for LSGDMChapter 7. Integration of Independent and Supervised Consensus ModelsChapter 8. Consensus Building: Coordination Between Trust Relationships and Opinion SimilarityChapter 9. Conclusions and Future Research Directions