Fuzzy Sets, Rough Sets, Multisets and Clustering
Published on 13. January 2017
X, 347 pages
978-3-319-47557-8 (ISBN)
Description
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.
More details
Series
Edition
1st ed. 2017
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Product notice
Reflowable
Illustrations
X, 347 p. 40 illus., 15 illus. in color.
File size
6,22 MB
ISBN-13
978-3-319-47557-8 (9783319475578)
DOI
10.1007/978-3-319-47557-8
Schweitzer Classification
Other editions
Additional editions
Vicenç Torra | Anders Dahlbom | Yasuo Narukawa
Fuzzy Sets, Rough Sets, Multisets and Clustering
Book
01/2017
Springer
€160.49
Shipment within 10-15 days
Content
On this book: clustering, multisets, rough sets and fuzzy sets.- Part 1: Clustering and Classi?cation.- Contributions of Fuzzy Concepts to Data Clustering.- Fuzzy Clustering/Co-clustering and Probabilistic Mixture Models-induced Algorithms.- Semi-Supervised Fuzzy c-Means Algorithms by Revising Dissimilarity/Kernel Matrices.- Various Types of Objective-Based Rough Clustering.- On Some Clustering Algorithms Based on Tolerance.- Robust Clustering Algorithms Employing Fuzzy-Possibilistic Product Partition.- Consensus-based agglomerative hierarchical clustering.- Using a reverse engineering type paradigm in clustering. An evolutionary pro-gramming based approach.- On Hesitant Fuzzy Clustering and Clustering of Hesitant Fuzzy Data.- Experiences using Decision Trees for Knowledge Discovery.- Part 2: Bags, Fuzzy Bags, and Some Other Fuzzy Extensions.- L-fuzzy Bags.- A Perspective on Differences between Atanassov's Intuitionistic Fuzzy Sets and Interval-valued Fuzzy Sets.- Part 3: Rough Sets.-Attribute Importance Degrees Corresponding to Several Kinds of Attribute Reduction in the Setting of the Classical Rough Sets.- A Review on Rough Set-based Interrelationship Mining.- Part 4: Fuzzy sets and decision making.- OWA Aggregation of Probability Distributions Using the Probabilistic Exceedance Method.- A dynamic average value-at-risk portfolio model with fuzzy random variables.- Group Decision Making: Consensus Approaches based on Soft Consensus Measures.- Construction of capacities from overlap indexes.- Clustering alternatives and learning preferences based on decision attitudes and weighted overlap dominance.
