Clustering analysis is a key technique in reducing the dimensionality of high volume irregular data containing large-scale group decision-making (LSGDM) information. Uncertainty theory is suitable for subjective estimation or situation, such as lack of historical data, and it can be employed to effectively express the uncertainty of trust and preference information in LSGDM problems. This paper studies the dimensionality reduction and subgroup optimization in LSGDM by utilizing linear uncertain variables in social networks. A clustering method is proposed to decompose the large group into several subgroups of higher consilience degrees and higher preference similarities, and lower the dimension of information for LSGDM. In the clustering process, two measurement attributes, trust relationship and preference relationship of decision-makers, are combined, and information consistent degree is utilized as the clustering indicator. This approach does not need to preset the threshold and the number of subgroups, and can be employed to obtain subgroups with similar preferences and stable trust relationship. Through the clustering reliability evaluation of subgroups, the rationality of large-scale group clustering results is verified. Subgroup consensus contribution is used to identify superior subgroups and quantify the role of subgroups in improving the consensus level. An example of emergency decision-making and comparative analysis is provided to explain the feasibility and advantages of the proposed method.

中文翻译：

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基于信息一致度的线性不确定分布信息大规模群决策聚类方法

聚类分析是降低包含大规模群体决策 (LSGDM) 信息的大量不规则数据的维数的关键技术。不确定性理论适用于主观估计或缺乏历史数据等情况，可用于有效表达 LSGDM 问题中信任和偏好信息的不确定性。本文利用社交网络中的线性不确定变量研究了LSGDM中的降维和子组优化。提出一种聚类方法，将大组分解为若干个一致性程度较高、偏好相似度较高的子组，降低LSGDM的信息维数。在聚类过程中，决策者的信任关系和偏好关系这两个度量属性，进行组合，以信息一致度作为聚类指标。这种方法不需要预先设定阈值和子群数量，可以用来获得偏好相似、信任关系稳定的子群。通过对子群的聚类可靠性评价，验证了大规模群聚类结果的合理性。亚组共识贡献用于识别优越亚组并量化亚组在提高共识水平中的作用。以应急决策和比较分析为例，说明所提方法的可行性和优势。并且可以用于获得具有相似偏好和稳定信任关系的子群。通过对子群的聚类可靠性评价，验证了大规模群聚类结果的合理性。亚组共识贡献用于识别优越亚组并量化亚组在提高共识水平中的作用。以应急决策和比较分析为例，说明所提方法的可行性和优势。并且可以用于获得具有相似偏好和稳定信任关系的子群。通过对子群的聚类可靠性评价，验证了大规模群聚类结果的合理性。亚组共识贡献用于识别优越亚组并量化亚组在提高共识水平中的作用。以应急决策和比较分析为例，说明所提方法的可行性和优势。