Fuzzy Sets, Rough Sets, Multisets and Clustering (inbunden)
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Format
Inbunden (Hardback)
Språk
Engelska
Antal sidor
347
Utgivningsdatum
2017-01-19
Upplaga
1st ed. 2017
Förlag
Springer International Publishing AG
Medarbetare
Torra, Vicenç (ed.), Dahlbom, Anders (ed.), Narukawa, Yasuo (ed.)
Illustratör/Fotograf
Bibliographie
Illustrationer
15 Illustrations, color; 25 Illustrations, black and white; X, 347 p. 40 illus., 15 illus. in color.
Dimensioner
234 x 156 x 21 mm
Vikt
681 g
Antal komponenter
1
Komponenter
1 Hardback
ISBN
9783319475561

Fuzzy Sets, Rough Sets, Multisets and Clustering

av Vicen Torra, Anders Dahlbom, Yasuo Narukawa
Inbunden,  Engelska, 2017-01-19
1645
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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.
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Vicen Torra, Anders Dahlbom, Yasuo Narukawa

Innehållsförteckning

On this book: clustering, multisets, rough sets and fuzzy sets.- Part 1: Clustering and Classication.- 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 Atanassovs 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.

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