2022
DOI: 10.1016/j.engappai.2021.104532
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An interactive consensus reaching model with updated weights of clusters in large-scale group decision making

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Cited by 46 publications
(4 citation statements)
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“…It also aims to find groups in the data, with the number of groups represented by the variable K (Cerqueti & Ficcadenti, 2022). According to Liao et al (2022), the variable K is the desired number of clusters. This Algorithm Cluster divides the data into several groups and receives input in the form of data without class labels.…”
Section: Methodsmentioning
confidence: 99%
“…It also aims to find groups in the data, with the number of groups represented by the variable K (Cerqueti & Ficcadenti, 2022). According to Liao et al (2022), the variable K is the desired number of clusters. This Algorithm Cluster divides the data into several groups and receives input in the form of data without class labels.…”
Section: Methodsmentioning
confidence: 99%
“…Depending on the implementation of different consensus rules, CRP models can be divided into two types, i.e., identification-direction consensus rules and optimization-based CRP models [30]. Despite the classical consensus model in which the facilitator assumes control of the CRP and provides advice to DMs [31,32], both facilitator and feedback mechanisms are obsolete in environments involving hundreds or thousands of DMs because they are too time-consuming and not feasible in practice. Therefore, in the context of LSGDM, the classical consensus model as an iterative discussion process should be replaced by automated algorithms that do not involve discussion rounds, moderators, or approval of DMs to change their opinions [10].…”
Section: Introductionmentioning
confidence: 99%
“…Currently, the research on MADM in complex environments and fuzzy information backgrounds has attracted great attention from more and more scholars at home and abroad [8][9][10][11]. However, due to the complex reality environment and the increase of uncertainty [12][13][14][15], as well as the subjective randomness of people's thinking, the traditional MADM method has certain limitations [16][17][18][19]. In order to better describe uncertain information, Zadeh [20] brought forward the theory of fuzzy sets (FSs) in 1965, which mainly represents information through the membership function of things.…”
Section: Introductionmentioning
confidence: 99%