Fuzzy sets, rough sets, multisets and clustering / Vicenç Torra, Anders Dahlbom, Yasuo Narukawa, editors.
Torra, Vicenç, editor.; Dahlbom, Anders, editor.; Narukawa, Yasuo, editor.
2017
QA248
Available Online

Linked e-resources

Linked Resource Online Access
Concurrent users Unlimited
Authorized users Authorized users
Document Delivery Supplied Can lend chapters, not whole ebooks

Details

Title Fuzzy sets, rough sets, multisets and clustering / Vicenç Torra, Anders Dahlbom, Yasuo Narukawa, editors.
ISBN 9783319475578 (electronic book)
3319475576 (electronic book)
9783319475561
DOI https://doi.org/10.1007/978-3-319-47557-8
Published Cham, Switzerland : Springer, 2017.
Language English
Description 1 online resource (x, 347 pages) : illustrations.
Other Standard Identifiers 10.1007/978-3-319-47557-8 doi
Call Number QA248
Dewey Decimal Classification 511.3/223
Summary 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.
Access Note Access limited to authorized users.
Digital File Characteristics text file PDF
Source of Description Online resource; title from PDF title page (SpringerLink, viewed January 24, 2017).
Added Author Torra, Vicenç, editor.
Dahlbom, Anders, editor.
Narukawa, Yasuo, editor.
Series Studies in computational intelligence ; v. 671.
Available in Other Form Print version: 9783319475561
Linked Resources Online Access
Record Appears in Online Resources > Ebooks
All Resources
On this book: clustering, multisets, rough sets and fuzzy sets
Part 1: Clustering and Classification
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.

Browse Subjects

Show more subjects...

Statistics

from
to
Export