000			03075nam a22003735i 4500
001			102159
003			DE-He213
005			20240111050137.0
007			cr nn 008mamaa
008			171229s2018 gw | s |||| 0|eng d
020			_a9783319693088
024	7		_a10.1007/978-3-319-69308-8 _2doi
040			_aES-MaUEC _bspa
050		4	_aQ342 2018 EB
100	1		_aWierzchoń, Slawomir. _eautor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _1http://viaf.org/viaf/18093691/
245	1	0	_aModern Algorithms of Cluster Analysis _cby Slawomir Wierzchoń, Mieczyslaw Kłopotek.
264		1	_aCham _bSpringer International Publishing _c2018
300			_a1 recurso en línea (XX, 421 páginas 51 ilustraciones)
347			_atext file _bPDF
490	0		_aStudies in Big Data, _x2197-6503 _v34
520	3		_aThis book provides the reader with a basic understanding of the formal concepts of the cluster, clustering, partition, cluster analysis etc.   The book explains feature-based, graph-based and spectral clustering methods and discusses their formal similarities and differences. Understanding the related formal concepts is particularly vital in the epoch of Big Data; due to the volume and characteristics of the data, it is no longer feasible to predominantly rely on merely viewing the data when facing a clustering problem.   Usually clustering involves choosing similar objects and grouping them together. To facilitate the choice of similarity measures for complex and big data, various measures of object similarity, based on quantitative (like numerical measurement results) and qualitative features (like text), as well as combinations of the two, are described, as well as graph-based similarity measures for (hyper) linked objects and measures for multilayered graphs. Numerous variants demonstrating how such similarity measures can be exploited when defining clustering cost functions are also presented.   In addition, the book provides an overview of approaches to handling large collections of objects in a reasonable time. In particular, it addresses grid-based methods, sampling methods, parallelization via Map-Reduce, usage of tree-structures, random projections and various heuristic approaches, especially those used for community detection.
650		7	_9666321 _aIngeniería asistida por ordenador
650		7	_aDatos masivos _9495511
650		7	_aMatemáticas _2embne _9405008
650		7	_aInteligencia artificial _2embne _9413115
700	1		_aKłopotek, Mieczyslaw. _eautor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _1http://viaf.org/viaf/101791521/
776	0	8	_iEdición impresa: _z9783319693071
776	0	8	_iEdición impresa: _z9783319693095
776	0	8	_iEdición impresa: _z9783319887524
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-319-69308-8 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
490	0		_aEngineering (Springer-11647)
988			_aEBSPRINGER_2018
998			_b12/2018 _dz _ea _feng _ggw _h0
999			_c102159 _d102159 _x1