東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁 到查詢結果 [ subject:"Group decision making- Mathematical models." ]

切換: 標籤 | MARC模式 | ISBD

Large-scale group decision-making = ...

Yu, Su-Min.

FindBook

Google Book

Amazon

博客來

Large-scale group decision-making = state-to-the-art clustering and consensus paths /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Large-scale group decision-making/ by Su-Min Yu, Zhi-Jiao Du.
其他題名:	state-to-the-art clustering and consensus paths /
作者:	Yu, Su-Min.
其他作者:	Du, Zhi-Jiao.
出版者:	Singapore :Springer Singapore : : 2022.,
面頁冊數:	xxiv, 179 p. :ill., digital ;24 cm.
內容註:	Chapter 1. Introduction -- Chapter 2. Preliminary Knowledge -- Chapter 3. Trust-Similarity Analysis-Based Clustering Method -- Chapter 4. Trust-Similarity Measure-Based Hierarchical Clustering Method -- Chapter 5. Hierarchical Punishment-Driven Consensus Model for Probabilistic Linguistic LSGDM -- Chapter 6. Confidence Consensus-Based Model for LSGDM -- Chapter 7. Integration of Independent and Supervised Consensus Models -- Chapter 8. Consensus Building: Coordination Between Trust Relationships and Opinion Similarity -- Chapter 9. Conclusions and Future Research Directions.
Contained By:	Springer Nature eBook
標題:	Group decision making - Mathematical models. -
電子資源:	https://doi.org/10.1007/978-981-16-7889-9
ISBN:	9789811678899

Large-scale group decision-making = state-to-the-art clustering and consensus paths /
Yu, Su-Min.

Large-scale group decision-makingstate-to-the-art clustering and consensus paths /[electronic resource] :by Su-Min Yu, Zhi-Jiao Du. - Singapore :Springer Singapore :2022. - xxiv, 179 p. :ill., digital ;24 cm.

Chapter 1. Introduction -- Chapter 2. Preliminary Knowledge -- Chapter 3. Trust-Similarity Analysis-Based Clustering Method -- Chapter 4. Trust-Similarity Measure-Based Hierarchical Clustering Method -- Chapter 5. Hierarchical Punishment-Driven Consensus Model for Probabilistic Linguistic LSGDM -- Chapter 6. Confidence Consensus-Based Model for LSGDM -- Chapter 7. Integration of Independent and Supervised Consensus Models -- Chapter 8. Consensus Building: Coordination Between Trust Relationships and Opinion Similarity -- Chapter 9. Conclusions and Future Research Directions.

This book explores clustering operations in the context of social networks and consensus-reaching paths that take into account non-cooperative behaviors. This book focuses on the two key issues in large-scale group decision-making: clustering and consensus building. Clustering aims to reduce the dimension of a large group. Consensus reaching requires that the divergent individual opinions of the decision makers converge to the group opinion. This book emphasizes the similarity of opinions and social relationships as important measurement attributes of clustering, which makes it different from traditional clustering methods with single attribute to divide the original large group without requiring a combination of the above two attributes. The proposed consensus models focus on the treatment of non-cooperative behaviors in the consensus-reaching process and explores the influence of trust loss on the consensus-reaching process.The logic behind is as follows: firstly, a clustering algorithm is adopted to reduce the dimension of decision-makers, and then, based on the clusters' opinions obtained, a consensus-reaching process is carried out to obtain a decision result acceptable to the majority of decision-makers. Graduates and researchers in the fields of management science, computer science, information management, engineering technology, etc., who are interested in large-scale group decision-making and consensus building are potential audience of this book. It helps readers to have a deeper and more comprehensive understanding of clustering analysis and consensus building in large-scale group decision-making.

ISBN: 9789811678899

Standard No.: 10.1007/978-981-16-7889-9doiSubjects--Topical Terms:

3216508
Group decision making
--Mathematical models.

LC Class. No.: HD30.23 / .Y8 2022

Dewey Class. No.: 658.4030015118

Large-scale group decision-making = state-to-the-art clustering and consensus paths /
LDR:03274nmm a2200337 a 4500 001 2296781
003 DE-He213
005 20220103192510.0
006 m d
007 cr nn 008maaau
008 230324s2022 si s 0 eng d
020 $a 9789811678899 $q (electronic bk.)
020 $a 9789811678882 $q (paper)
024 7 $a 10.1007/978-981-16-7889-9 $2 doi
035 $a 978-981-16-7889-9
040 $a GP $c GP
041 0 $a eng
050 4 $a HD30.23 $b .Y8 2022
072 7 $a KJT $2 bicssc
072 7 $a BUS049000 $2 bisacsh
072 7 $a KJT $2 thema
072 7 $a KJMD $2 thema
082 0 4 $a 658.4030015118 $2 23
090 $a HD30.23 $b .Y94 2022
100 1 $a Yu, Su-Min. $3 3591707
245 1 0 $a Large-scale group decision-making $h [electronic resource] : $b state-to-the-art clustering and consensus paths / $c by Su-Min Yu, Zhi-Jiao Du.
260 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2022.
300 $a xxiv, 179 p. : $b ill., digital ; $c 24 cm.
505 0 $a Chapter 1. Introduction -- Chapter 2. Preliminary Knowledge -- Chapter 3. Trust-Similarity Analysis-Based Clustering Method -- Chapter 4. Trust-Similarity Measure-Based Hierarchical Clustering Method -- Chapter 5. Hierarchical Punishment-Driven Consensus Model for Probabilistic Linguistic LSGDM -- Chapter 6. Confidence Consensus-Based Model for LSGDM -- Chapter 7. Integration of Independent and Supervised Consensus Models -- Chapter 8. Consensus Building: Coordination Between Trust Relationships and Opinion Similarity -- Chapter 9. Conclusions and Future Research Directions.
520 $a This book explores clustering operations in the context of social networks and consensus-reaching paths that take into account non-cooperative behaviors. This book focuses on the two key issues in large-scale group decision-making: clustering and consensus building. Clustering aims to reduce the dimension of a large group. Consensus reaching requires that the divergent individual opinions of the decision makers converge to the group opinion. This book emphasizes the similarity of opinions and social relationships as important measurement attributes of clustering, which makes it different from traditional clustering methods with single attribute to divide the original large group without requiring a combination of the above two attributes. The proposed consensus models focus on the treatment of non-cooperative behaviors in the consensus-reaching process and explores the influence of trust loss on the consensus-reaching process.The logic behind is as follows: firstly, a clustering algorithm is adopted to reduce the dimension of decision-makers, and then, based on the clusters' opinions obtained, a consensus-reaching process is carried out to obtain a decision result acceptable to the majority of decision-makers. Graduates and researchers in the fields of management science, computer science, information management, engineering technology, etc., who are interested in large-scale group decision-making and consensus building are potential audience of this book. It helps readers to have a deeper and more comprehensive understanding of clustering analysis and consensus building in large-scale group decision-making.
650 0 $a Group decision making $x Mathematical models. $3 3216508
650 0 $a Cluster analysis. $3 562995
650 1 4 $a Operations Research/Decision Theory. $3 890895
650 2 4 $a Operations Research, Management Science. $3 1532996
650 2 4 $a Computer Science, general. $3 892601
700 1 $a Du, Zhi-Jiao. $3 3591708
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-981-16-7889-9
950 $a Business and Management (SpringerNature-41169)