Trust Cop-Kmeans Clustering Analysis and Minimum-Cost Consensus Model Considering Voluntary Trust Loss in Social Network Large-Scale Decision-Making
| Autor: | Han-yang Luo, Zhi-jiao Du, Xu-dong Lin, Xue-yang Zhang, Su-min Yu |
|---|---|
| Rok vydání: | 2022 |
| Předmět: |
Social network
business.industry Computer science Process (engineering) Applied Mathematics Decision theory k-means clustering Machine learning computer.software_genre Constraint (information theory) Resource (project management) Computational Theory and Mathematics Artificial Intelligence Control and Systems Engineering Scale (social sciences) Artificial intelligence business Cluster analysis computer |
| Zdroj: | IEEE Transactions on Fuzzy Systems. 30:2634-2648 |
| ISSN: | 1941-0034 1063-6706 |
| DOI: | 10.1109/tfuzz.2021.3089745 |
| Popis: | Social network large-scale decision-making (SN-LSDM) has attracted much attention in the field of decision science. Clustering and consensus building are two important processes for solving SN-LSDM problems. Traditional clustering methods are typically based on the opinion similarity. When extending decision-making to social network contexts, we argue that trust relationships among decision-makers (DMs) serve as a reliable resource for clustering. This study proposes a trust Cop-Kmeans clustering algorithm, which is a semi-supervised clustering technique using prior knowledge of trust constraints generated by trust relationships. The consensus-reaching process (CRP) is an effective tool for reducing differences of opinion. In general, the costs and resources associated with the CRP are limited. Therefore, minimum-cost consensus (MCC) models have been developed and used widely in various group decision-making contexts. As an important resource influencing decision-making, trust provides a common-sense perception that the opinion of a DM with a high trust degree is considered to be widely recognized by others. We hold that the DM with high trust and low consensus can reduce the consensus cost by voluntarily losing some trust. Consequently, an improved MCC model considering voluntary trust loss is developed. Finally, a numerical example is presented to illustrate the feasibility of the proposed clustering algorithm and consensus model. A comparative analysis is implemented to explore the influence of the trust constraint on clustering and the influence of trust loss on the CRP. |
| Databáze: | OpenAIRE |
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