CLUSTERING ALGORITHM FOR LARGE GROUPS OF EXPERTS BASED ON THE INTERPRETIVE STRUCTURAL MODELING METHOD
Abstract
This article presents an algorithm for achieving consensus in social networks during large‑scale group decision‑making with incomplete probabilistic fuzzy information containing elements of uncertainty, which takes into account the trust relationships among experts. A method for clustering experts based on interpretive structural modelling is proposed. It serves both to classify experts and to enhance the efficiency of consensus achievement.The study examines trust propagation and aggregation operators for probabilistic fuzzy information with elements of uncertainty. These operators enable indirect trust assessment and determination of experts’ weight coefficients. As a result, it becomes possible to form several subsets of experts and to determine weight coefficients for a large number of experts based on their mutual trust relationships. Based on the clustering of experts and the calculated indirect trust relationship between experts, decision‑making in emergency situations is carried out by achieving consensus, taking into account fluctuating probabilistic fuzzy information, and the best evacuation alternative is identified. The assessments provided by experts in the form of probabilistic fluctuating fuzzy values allow for effective modelling of doubts, uncertainty, and inconsistencies in expert evaluations when a group of experts or various expert organisations are involved. At the same time, it becomes possible to take into account different expert assessment values in multi‑criteria decision‑making tasks when experts cannot agree on common membership degrees. The algorithm allows classifying a large group of experts into several subsets based on their social trust relationships. This method prevents the formation of overlapping subsets and does not require pre‑setting clustering parameters. It relies exclusively on social trust relationships between experts, thereby avoiding the issue of subjective intervention in the clustering process. Compared to traditional clustering methods, the interpretive structural modelling‑based clustering approach effectively reveals the hierarchical structure of relationships among experts. It also minimizes the number of participants in large‑scale group decision‑making within a social network by reducing the dimensionality of the expert set. Clustering experts based on the interpretive structural modelling method significantly enhances the efficiency and feasibility of large‑scale group decision‑making
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