LEADER 04341nam 22005895 450 001 9910254171703321 005 20251113194733.0 010 $a3-319-47557-6 024 7 $a10.1007/978-3-319-47557-8 035 $a(CKB)3710000001041153 035 $a(DE-He213)978-3-319-47557-8 035 $a(MiAaPQ)EBC6304141 035 $a(MiAaPQ)EBC5592731 035 $a(Au-PeEL)EBL5592731 035 $a(OCoLC)969844365 035 $a(PPN)198340478 035 $a(EXLCZ)993710000001041153 100 $a20170113d2017 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aFuzzy Sets, Rough Sets, Multisets and Clustering /$fedited by Vicenç Torra, Anders Dahlbom, Yasuo Narukawa 205 $a1st ed. 2017. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2017. 215 $a1 online resource (X, 347 p. 40 illus., 15 illus. in color.) 225 1 $aStudies in Computational Intelligence,$x1860-9503 ;$v671 311 08$a3-319-47556-8 320 $aIncludes bibliographical references. 327 $aOn 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. 330 $aThis book is dedicated to Prof. Sadaaki Miyamoto and presents cutting-edge papers in some of the areas in which he contributed. Bringing together contributions by leading researchers in the field, it concretely addresses clustering, multisets, rough sets and fuzzy sets, as well as their applications in areas such as decision-making. The book is divided in four parts, the first of which focuses on clustering and classification. The second part puts the spotlight on multisets, bags, fuzzy bags and other fuzzy extensions, while the third deals with rough sets. Rounding out the coverage, the last part explores fuzzy sets and decision-making. 410 0$aStudies in Computational Intelligence,$x1860-9503 ;$v671 606 $aComputational intelligence 606 $aArtificial intelligence 606 $aComputational Intelligence 606 $aArtificial Intelligence 615 0$aComputational intelligence. 615 0$aArtificial intelligence. 615 14$aComputational Intelligence. 615 24$aArtificial Intelligence. 676 $a511.322 702 $aTorra$b Vicenç$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aDahlbom$b Anders$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aNarukawa$b Yasuo$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910254171703321 996 $aFuzzy Sets, Rough Sets, Multisets and Clustering$92050579 997 $aUNINA