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Majority Clusters‐Density Ordered Weighting Averaging: A Family of New Aggregation Operators in Group Decision Making
Author(s) -
Li Weiwei,
Yi Pingtao,
Guo Yajun
Publication year - 2016
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.21821
Subject(s) - weighting , group decision making , cluster analysis , cluster (spacecraft) , group (periodic table) , class (philosophy) , mathematics , computer science , operator (biology) , data mining , artificial intelligence , physics , chemistry , quantum mechanics , biochemistry , repressor , political science , acoustics , transcription factor , law , gene , programming language
In the process of aggregation, it is necessary, especially for group decision‐making (GDM) problems, to consider distributed characteristic hidden in aggregates. In this case, clustering has been a common way for discovering the implicit distributed structures. This paper mainly investigates the characteristic of majority clusters, rather than majority elements and develops a new class of aggregation operators denominated majority clusters density‐ordered weighting averaging (MC‐DOWA) operators. Furthermore, we discuss properties of these operators and calculate the associated weights. Finally, a numerical example is provided to illustrate the application of the MC‐DOWA operators, and the aggregations are compared with those of the other three aggregation operators: majority additive‐OWA (MA‐OWA), dependent OWA (DOWA) and cluster‐based DOWA (Clus‐DOWA) operators.