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Fuzzy sets, rough sets, multisets an...

Dahlbom, Anders.

Fuzzy sets, rough sets, multisets and clustering

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Fuzzy sets, rough sets, multisets and clusteringedited by Vicenc Torra, Anders Dahlbom, Yasuo Narukawa.
其他作者:	Torra, Vicenc.
出版者:	Cham :Springer International Publishing :2017.
面頁冊數:	x, 347 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
標題:	Fuzzy sets.
電子資源:	http://dx.doi.org/10.1007/978-3-319-47557-8
ISBN:	9783319475578$q(electronic bk.)

Fuzzy sets, rough sets, multisets and clustering
Fuzzy sets, rough sets, multisets and clustering[electronic resource] /edited by Vicenc Torra, Anders Dahlbom, Yasuo Narukawa. - Cham :Springer International Publishing :2017. - x, 347 p. :ill. (some col.), digital ;24 cm. - Studies in computational intelligence,v.6711860-949X ;. - Studies in computational intelligence ;v. 216..

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.

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.

ISBN: 9783319475578$q(electronic bk.)

Standard No.: 10.1007/978-3-319-47557-8doiSubjects--Topical Terms:

182529
Fuzzy sets.

LC Class. No.: QA248

Dewey Class. No.: 511.3223

Fuzzy sets, rough sets, multisets and clustering
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505 0 $a 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.
520 $a 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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650 2 4 $a Artificial Intelligence (incl. Robotics) $3 252959
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