Intuitionistic Fuzzy Aggregation and Clustering

by Zeshui Xu

Book Overview

1 Intuitionistic Fuzzy Aggregation Techniques.-1.1 Rankings of Intuitionistic Fuzzy Values.-1.1.1 Intuitionistic Fuzzy Values.-1.1.2 Methods for Ranking IFVs.-1.1.2.1 The Method for Ranking IFVs by Using the Score Function.-1.2.1.2 The Method for Ranking IFVs by Using the Positive Ideal Point.-1.1.2.3 The Method for Ranking IFVs by Using the Intuitionistic Fuzzy Point Operators.-1.1.2.4 The Method for Ranking IFVs by Using the Similarity Measure and the Accuracy Degree.-1.1.3 The Application of Ranking IFVs Using the Similarity Measure and the Accuracy Degree in Multi-attribute Decision Making.-1.2 Intuitionistic Fuzzy Power Aggregation Operators.-1.2.1 Power Aggregation Operatores.-1.2.2 Some Operational Laws of IFVs.-1.2.3 Power Aggregation Operators for IFVs.-1.2.4 Approaches to Multi-attribute Group Decision Making with Intuitionistic Fuzzy Information.-1.2.5 Practical Example.-1.3 Interval-valued Intuitionistic Fuzzy Power Aggregation Operators.-1.3.1 Interval-valued Intuitionistic Fuzzy Values .-1.3.2 Power Aggregation Operators for IVIFVs.-1.3.3 Approaches to Multi-attribute Group Decision Making with Interval-valued Intuitionistic Fuzzy Information.-1.4 Intuitionistic Fuzzy Geometric Bonferroni Means.-1.4.1 Geometric Bonferroni Mean.-1.4.2 Intuitionistic Fuzzy Geometric Bonferroni Mean.-1.4.3 The Weighted Intuitionistic Fuzzy Geometric Bonferroni Mean and Its Application in Multi-attribute Decision Making.-1.5 Generalized Intuitionistic Fuzzy Bonferroni Means.-1.5.1 Generalized Bonferroni Means.-1.5.2 Generalized Intuitionistic Fuzzy Weighted Bonferroni Mean.-1.5.3 Generalized Intuitionistic Fuzzy Weighted Bonferroni Geometric Mean.-1.6 Intuitionistic Fuzzy Aggregation Operators Based on Archimedean t-conorm and t-norm.-1.6.1 Intuitionistic Fuzzy Operational Laws Based on t-conorm and t-norm.-1.6.2 Intuitionistic Fuzzy Aggregation Operators Based on Archimedean t-conorm and t-norm.-1.6.3 An Approach to Intuitionistic Fuzzy Multi-attribute Decision Making.-1.7 Generalized Intuitionistic Fuzzy Aggregation Operators Based on Hamacher t-conorm and t-norm.-1.8 Point Operators for Aggregation IFVs.-1.9 Generalized Point Operators for Aggregating IFVs.-2 Intuitionistic Fuzzy Clustering Algorithms.-2.1Clustering Algorithms Based on Intuitionistic Fuzzy Similarity Matrices.-2.2 Clustering Algorithms Based on Association Matrices.-2.3 Intuitionistic Fuzzy Hierarchical Clustering Algorithms.-2.4 Intuitionistic Fuzzy Orthogonal Clustering Algorithm.-2.5 Intuitionistic Fuzzy C-Means Clustering Algorithms.-2.6 Intuitionistic Fuzzy MST Clustering Algorithm.-2.7 Intuitionistic Fuzzy Clustering Algorithm Based on Boole Matrix and Association Measure.-2.7.1 Intuitionistic Fuzzy Association Measures.-2.7.2 Intuitionistic Fuzzy Clustering Algorithm.-2.7.3 Numerical Example.-2.7.4 Interval-Valued Intuitionistic Fuzzy Clustering Algorithm.-2.8 A Netting Method for Clustering Intuitionistic Fuzzy Information.-2.8.1 A New Approach to Constructing Intuitionistic Fuzzy Similiarity Matrix.-2.8.2 A Netting Clustering Method.-2.8.3 Illustrative Examples.-2.9 Direct Cluster Analysis Based on Intuitionistic Fuzzy Implication.-2.9.1 The Intuitionistic Fuzzy Implication Operator and Intuitionistic Fuzzy Products.-2.9.1 The Application of Two Intuitionistic Fuzzy Products.-2.9.2 The Application of Two Intuitionistic Fuzzy Products.-2.9.3 The Application of the Intuitionistic Fuzzy Triangle Products.-2.9.4 The Application of the Intuitionistic Square Product.-2.9.5 A Direct Intuitionistic Fuzzy Cluster Analysis Method.-References.

• Format: Paperback
• Language: English
• ISBN: 3642436129
• ISBN13: 9783642436123
• Release Date: June 2015
• Publisher: Springer
• Length: 278 Pages
• Weight: 0.90 lbs.
• Dimensions: 0.6" x 6.1" x 9.2"