Fuzzy Sets, Rough Sets, Multisets and Clustering

Torra, Vicenç (Edited by)/ Dahlbom, Anders (Edited by)/ Narukawa, Yasuo (Edited by)

ISBN 10: 3319475568 / ISBN 13: 9783319475561

Edité par Springer, 2017

A propos de cet article

347 pages. 9.50x6.50x1.00 inches. In Stock. N° de réf. du vendeur x-3319475568

Détails bibliographiques

Titre : Fuzzy Sets, Rough Sets, Multisets and ...

Éditeur : Springer

Date d'édition : 2017

Reliure : Hardcover

Etat : Brand New

A propos de ce titre

Synopsis :

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.