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001 978-3-642-35536-3
003 DE-He213
005 20200420221255.0
007 cr nn 008mamaa
008 130418s2013 gw | s |||| 0|eng d
020 _a9783642355363
_9978-3-642-35536-3
024 7 _a10.1007/978-3-642-35536-3
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
082 0 4 _a006.3
_223
100 1 _aViattchenin, Dmitri A.
_eauthor.
245 1 2 _aA Heuristic Approach to Possibilistic Clustering: Algorithms and Applications
_h[electronic resource] /
_cby Dmitri A. Viattchenin.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2013.
300 _aXII, 227 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStudies in Fuzziness and Soft Computing,
_x1434-9922 ;
_v297
505 0 _aIntroduction -- Heuristic Algorithms of Possibilistic Clustering -- Clustering Approaches for the Uncertain Data -- Applications of the Heuristic Algorithms of Possibilistic Clustering.
520 _aThe present book outlines a new approach to possibilistic clustering in which the sought clustering structure of the set of objects is based directly on the formal definition of fuzzy cluster and the possibilistic memberships are determined directly from the values of the pairwise similarity of objects.   The proposed approach can be used for solving different classification problems. Here, some techniques that might be useful at this purpose are outlined, including a methodology for constructing a set of labeled objects for a semi-supervised clustering algorithm, a methodology for reducing analyzed attribute space dimensionality and a methods for asymmetric data processing. Moreover,  a technique for constructing a subset of the most appropriate alternatives for a set of weak fuzzy preference relations, which are defined on a universe of alternatives, is described in detail, and a method for rapidly prototyping the Mamdani's fuzzy inference systems is introduced. This book addresses engineers, scientists, professors, students and post-graduate students, who are interested in and work with fuzzy clustering and its applications.
650 0 _aEngineering.
650 0 _aData mining.
650 0 _aArtificial intelligence.
650 0 _aComputational intelligence.
650 1 4 _aEngineering.
650 2 4 _aComputational Intelligence.
650 2 4 _aData Mining and Knowledge Discovery.
650 2 4 _aArtificial Intelligence (incl. Robotics).
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783642355356
830 0 _aStudies in Fuzziness and Soft Computing,
_x1434-9922 ;
_v297
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-642-35536-3
912 _aZDB-2-ENG
942 _cEBK
999 _c52838
_d52838