A Dynamic Programming Algorithm Based Clustering Model and Its Application to Interval Type-2 Fuzzy Large-Scale Group Decision-Making Problem

This article focuses on employing the dynamic programming algorithm to solve the large-scale group decision-making problems, where the preference information takes the form of linguistic variables. Specifically, considering the linguistic variables cannot be directly computed, the interval type-2 fu...

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Veröffentlicht in:	IEEE transactions on fuzzy systems Jg. 30; H. 1; S. 108 - 120
Hauptverfasser:	Pan, Xiaohong, Wang, Yingming, He, Shifan, Chin, Kwai-Sang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.01.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Centroid-based ranking method Centroids Clustering Clustering algorithms Clustering methods Decision making Dynamic programming dynamic programming algorithm-based clustering model Fuzzy sets Heuristic algorithms interval type-2 fuzzy sets large-scale group decision-making (GDM) Linguistics
ISSN:	1063-6706, 1941-0034
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Abstract	This article focuses on employing the dynamic programming algorithm to solve the large-scale group decision-making problems, where the preference information takes the form of linguistic variables. Specifically, considering the linguistic variables cannot be directly computed, the interval type-2 fuzzy sets are employed to encode them. Then, new distance model and similarity model are respectively developed to measure the relationships between the interval type-2 fuzzy sets. After that, a dynamic programming algorithm-based clustering model is proposed to cluster the decision-makers from the overall perspective. Moreover, by taking both the cluster center and the group size into consideration, a new model is introduced to determine the weights of clusters and decision-makers, respectively. Finally, a centroid-based ranking method is developed to compare and rank the alternatives, and two illustrative experiments are provided to illustrate the effectiveness of the proposed method. Comparisons and discussions are also conducted to verify its superiority.
AbstractList	This article focuses on employing the dynamic programming algorithm to solve the large-scale group decision-making problems, where the preference information takes the form of linguistic variables. Specifically, considering the linguistic variables cannot be directly computed, the interval type-2 fuzzy sets are employed to encode them. Then, new distance model and similarity model are respectively developed to measure the relationships between the interval type-2 fuzzy sets. After that, a dynamic programming algorithm-based clustering model is proposed to cluster the decision-makers from the overall perspective. Moreover, by taking both the cluster center and the group size into consideration, a new model is introduced to determine the weights of clusters and decision-makers, respectively. Finally, a centroid-based ranking method is developed to compare and rank the alternatives, and two illustrative experiments are provided to illustrate the effectiveness of the proposed method. Comparisons and discussions are also conducted to verify its superiority.
Author	Wang, Yingming Chin, Kwai-Sang Pan, Xiaohong He, Shifan
Author_xml	– sequence: 1 givenname: Xiaohong orcidid: 0000-0003-2898-6716 surname: Pan fullname: Pan, Xiaohong email: xiaohongpan0303@126.com organization: Fuzhou University of Decision Sciences Institute, Fujian, China – sequence: 2 givenname: Yingming surname: Wang fullname: Wang, Yingming email: ymwang@fzu.edu.cn organization: Fuzhou University of Decision Sciences Institute, Fujian, China – sequence: 3 givenname: Shifan surname: He fullname: He, Shifan email: shifanhe0828@163.com organization: Fuzhou University of Decision Sciences Institute, Fujian, China – sequence: 4 givenname: Kwai-Sang surname: Chin fullname: Chin, Kwai-Sang email: mekschin@cityu.edu.hk organization: City University of Hong Kong of Systems Engineering and Engineering Management, Hong Kong, Hong Kong
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SubjectTerms	Algorithms Centroid-based ranking method Centroids Clustering Clustering algorithms Clustering methods Decision making Dynamic programming dynamic programming algorithm-based clustering model Fuzzy sets Heuristic algorithms interval type-2 fuzzy sets large-scale group decision-making (GDM) Linguistics
Title	A Dynamic Programming Algorithm Based Clustering Model and Its Application to Interval Type-2 Fuzzy Large-Scale Group Decision-Making Problem
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