Outlier identification and group satisfaction of rating experts

Density-based spatial clustering of applications with noise based on multi-objective large-scale group decision-making evaluation

Dados Bibliográficos

ID15067090
AutoresShengjia Zhou (0009-0009-3077-5441, Zhejiang University of Finance and Economics), Junxing Zhou (Zhejiang University of Finance and Economics), Sichao Chen (0000-0002-7361-157X, Zhejiang Gongshang University, autor correspondente)
Ano2023
Volume36
Fascículo1
Páginas562-592
Data de publicação2023-03-31
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoEconomic Research-Ekonomska Istraživanja (JOURNAL)
Identificadores do periódicoISSN: 1331-677X • E-ISSN: 1848-9664
EditoraInforma UK Limited (PUBLISHER • GB)
DOI10.1080/1331677x.2022.2078850
OpenAlexW4281911133
IdiomaEN
Referências citadas41

Group satisfaction is a trending issue in large-scale group decision-making (LSGDM) but most existing studies maximize the group satisfaction of LSGDM from the perspective of consensus. However, the clustering algorithm in LSGDM also has an impact on group satisfaction. Hence, this paper proposes a density-based spatial clustering of applications with noise (DBSCAN)-based LSGDM approach in an intuitionistic fuzzy set (IFS) environment. The DBSCAN algorithm is used to identify experts with outlier ratings that can reduce the time consumption and iterations of the LSGDM process and maximize the satisfaction of the group decision. An easy-to-use function is then provided to estimate group satisfaction. Finally, a numerical example of data centre supplier evaluation and comparative analysis is constructed to validate the rationality and feasibility of the proposed DBSCAN-based LSGDM approach in an IFS environment. The results demonstrate that the proposed method can effectively identify outliers in expert ratings and improve group satisfaction in the LSGDM process

Cartography · Cluster analysis · Data mining · Geography · Group identification · Machine learning · Outlier · Rating scale · Statistics · Computer Science · Evaluation Methods in Various Fields · Mathematics · Multi-Criteria Decision Making · Psychology · Social Psychology · Artificial Intelligence

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