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Probabilistic linguistic large‐group decision‐making (LGDM) is becoming a hot topic in the field of decision science. In general, clustering and consensus‐reaching are the two important processes to deal with LGDM problems. Traditional clustering methods are mainly based on opinion similarity measures and cannot directly control the number of decision makers (DMs) that can be contained in a cluster. Situations of noncooperative behavior toward consensus may be encountered during the consensus‐reaching process. This paper first proposes a distance‐to‐center clustering algorithm to classify the large group. The most important characteristic of the algorithm is that it is based on similarity measures of probabilistic linguistic information, and specifies the upper and lower limits on the number of DMs in the cluster. Then, a novel weight‐determining method is developed that considers the size, external …