Large-scale group decision-making (LSGDM) solution is usually based on the clustering analysis process (CAP) and consensus reaching process (CRP). However, CAP and CRP can be contradictory since CAP is performed based on the differences between potentially small groups and CRP is conducted to improve the overall similarity of a large group. To balance CAP and CRP, a dynamic clustering analysis process (DCAP) based on consensus evolution networks is proposed. A clustering algorithm proposed based on community detection method can be used to handle the diverse network structures with dynamic consensus thresholds. The clustering validity based on the intracluster consensus levels in subgroups and the intercluster consensus level among subgroups is evaluated. Then, the DCAP after each feedback adjustment round in CRP is reanalyzed. In such a way, effective clustering can also be found after a satisfying consensus is reached. Finally, a case study shows the availability of this approach and comparative analyses are provided to highlight the advantages from both theoretical and numerical perspectives.

中文翻译：

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在大规模群体决策中利用共识进化网络平衡动态聚类分析和共识达成过程


大规模群体决策（LSGDM）解决方案通常基于聚类分析过程（CAP）和共识达成过程（CRP）。然而，CAP 和 CRP 可能是矛盾的，因为 CAP 是基于潜在小群体之间的差异进行的，而 CRP 是为了提高大群体的整体相似性而进行的。为了平衡CAP和CRP，提出了一种基于共识进化网络的动态聚类分析过程（DCAP）。基于社区检测方法提出的聚类算法可以用来处理具有动态共识阈值的多样化网络结构。评估基于子组内簇内共识水平和子组间簇间共识水平的聚类有效性。然后，重新分析CRP中每轮反馈调整后的DCAP。这样，在达成满意的共识后，也能找到有效的聚类。最后，案例研究表明了这种方法的可用性，并提供了比较分析，以从理论和数值角度强调其优势。