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008			170124s2017 sz a o 000 0 eng d
020			_a3319475568
020			_a3319475576 _q(electronic bk.)
020			_a9783319475561
020			_a9783319475578 _q(electronic bk.)
020			_z9783319475561 _q(print)
035			_a(OCoLC)969844365 _z(OCoLC)971532301 _z(OCoLC)971595594 _z(OCoLC)971963432 _z(OCoLC)974649514 _z(OCoLC)980993333 _z(OCoLC)981890054 _z(OCoLC)1005772825 _z(OCoLC)1012063177
040			_aGW5XE _cGW5XE _dYDX _dOCLCF _dUAB _dNJR _dCOO _dUPM _dVT2 _dUWO _dIOG _dESU _dJBG _dIAD _dICW _dICN _dOTZ _dOCLCQ _dU3W _dES-MaUEC _bspa
050		4	_aQA248 _b2017 EB
245	0	0	_aFuzzy sets, rough sets, multisets and clustering _cVicenç Torra, Anders Dahlbom, Yasuo Narukawa, editors.
264		1	_aCham, Switzerland _bSpringer _c2017
300			_a1 recurso en línea (x, 347 páginas) _bilustraciones (algunas a color)
336			_aTexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	0		_aStudies in computational intelligence _x1860-949X _vvolume 671
500			_aSpringerLink _bSpringer Engineering eBooks 2017 English+International
505	0		_aOn 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	3		_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.
650		7	_aLógica matemática _2embne _0(OCoLC)fst00864990 _0 _9139136
700	1		_aDahlbom, Anders, _eeditor literario
700	1		_aNarukawa, Yasuo, _eeditor literario
700	1		_aTorra, Vicenç, _eeditor literario
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=http://link.springer.com/10.1007/978-3-319-47557-8 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
988			_aEBOOK, asignarmaterias, EBSPRINGER_2017B
998			_b02/2018 _dz _e- _zSI
999			_c95254 _d95254 _x1