Fuzzy Sets, Rough Sets, Multisets and Clustering

✍ Scribed by Vicenç Torra, Anders Dahlbom, Yasuo Narukawa (eds.)

• Publisher: Springer International Publishing
• Year: 2017
• Tongue: English
• Leaves: 336
• Series: Studies in Computational Intelligence 671
• Edition: 1
• Category: Library

✦ Synopsis

This book is dedicated to Prof. Sadaaki Miyamoto and presents cutting-edge papers in some of the areas in which he contributed. Bringing together contributions by leading researchers in the field, it concretely addresses clustering, multisets, rough sets and fuzzy sets, as well as their applications in areas such as decision-making.

The book is divided in four parts, the first of which focuses on clustering and classification. The second part puts the spotlight on multisets, bags, fuzzy bags and other fuzzy extensions, while the third deals with rough sets. Rounding out the coverage, the last part explores fuzzy sets and decision-making.

✦ Table of Contents

Front Matter....Pages i-x
On This Book: Clustering, Multisets, Rough Sets and Fuzzy Sets....Pages 1-5
Front Matter....Pages 7-7
Contributions of Fuzzy Concepts to Data Clustering....Pages 9-28
Fuzzy Clustering/Co-clustering and Probabilistic Mixture Models-Induced Algorithms....Pages 29-43
Semi-supervised Fuzzy c-Means Algorithms by Revising Dissimilarity/Kernel Matrices....Pages 45-61
Various Types of Objective-Based Rough Clustering....Pages 63-85
On Some Clustering Algorithms Based on Tolerance....Pages 87-99
Robust Clustering Algorithms Employing Fuzzy-Possibilistic Product Partition....Pages 101-121
Consensus-Based Agglomerative Hierarchical Clustering....Pages 123-135
Using a Reverse Engineering Type Paradigm in Clustering. An Evolutionary Programming Based Approach....Pages 137-155
On Hesitant Fuzzy Clustering and Clustering of Hesitant Fuzzy Data....Pages 157-168
Experiences Using Decision Trees for Knowledge Discovery....Pages 169-191
Front Matter....Pages 193-193
L-Fuzzy Bags....Pages 195-219
A Perspective on Differences Between Atanassov’s Intuitionistic Fuzzy Sets and Interval-Valued Fuzzy Sets....Pages 221-237
Front Matter....Pages 239-239
Attribute Importance Degrees Corresponding to Several Kinds of Attribute Reduction in the Setting of the Classical Rough Sets....Pages 241-255
A Review on Rough Set-Based Interrelationship Mining....Pages 257-273
Front Matter....Pages 275-275
OWA Aggregation of Probability Distributions Using the Probabilistic Exceedance Method....Pages 277-289
A Dynamic Average Value-at-Risk Portfolio Model with Fuzzy Random Variables....Pages 291-306
Group Decision Making: Consensus Approaches Based on Soft Consensus Measures....Pages 307-321
Construction of Capacities from Overlap Indexes....Pages 323-335
Clustering Alternatives and Learning Preferences Based on Decision Attitudes and Weighted Overlap Dominance....Pages 337-347

✦ Subjects

Computational Intelligence;Artificial Intelligence (incl. Robotics)