Original scientific paper
https://doi.org/10.1080/1331677X.2022.2078850
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
Shengjia Zhou
Junxing Zhou
Sichao Chen
Abstract
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.
Keywords
DBSCAN; group satisfaction; large-scale group decision making; intuitionistic fuzzy sets; data centre supplier evaluation
Hrčak ID:
303744
URI
Publication date:
31.3.2023.
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