000			03521nam a22004335i 4500
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710	2		_aSpringerLink (Online service) _9106996
999			_c110880 _d110880 _x1
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003			ES-MaUEC
005			20230102113443.0
008			181027s2019 gw a o |||| 0|eng d
020			_a9783319978642
024	7		_a10.1007/978-3-319-97864-2 _2doi
040			_bspa _dES-MaUEC _cES-MaUEC
050		4	_aQA76.9.B45 _b2019 EB
245	0	0	_aClustering Methods for Big Data Analytics : _bTechniques, Toolboxes and Applications _cedited by Olfa Nasraoui, Chiheb-Eddine Ben N'Cir.
264		1	_aCham _bImprint: Springer _c2019
300			_a1 recurso en línea (IX, 187 páginas) _b63 ilustraciones, 31 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF
490	0		_aEngineering (Springer-11647)
490	0		_aUnsupervised and Semi-Supervised Learning _x2522-848X
505	0		_aIntroduction -- Clustering large scale data -- Clustering heterogeneous data -- Distributed clustering methods -- Clustering structured and unstructured data -- Clustering and unsupervised learning for deep learning -- Deep learning methods for clustering -- Clustering high speed cloud, grid, and streaming data -- Extension of partitioning, model based, density based, grid based, fuzzy and evolutionary clustering methods for big data analysis -- Large documents and textual data clustering -- Applications of big data clustering methods -- Clustering multimedia and multi-structured data -- Large-scale recommendation systems and social media systems -- Clustering multimedia and multi-structured data -- Real life applications of big data clustering -- Validation measures for big data clustering methods -- Conclusion.
520	3		_aThis book highlights the state of the art and recent advances in Big Data clustering methods and their innovative applications in contemporary AI-driven systems. The book chapters discuss Deep Learning for Clustering, Blockchain data clustering, Cybersecurity applications such as insider threat detection, scalable distributed clustering methods for massive volumes of data; clustering Big Data Streams such as streams generated by the confluence of Internet of Things, digital and mobile health, human-robot interaction, and social networks; Spark-based Big Data clustering using Particle Swarm Optimization; and Tensor-based clustering for Web graphs, sensor streams, and social networks. The chapters in the book include a balanced coverage of big data clustering theory, methods, tools, frameworks, applications, representation, visualization, and clustering validation. .
988			_aPrimersemestre_2019_Engineering
650		7	_2embne _9495511 _aDatos masivos
650		7	_2embne _9669583 _aAnálisis Cluster
700	1		_aBen N'Cir, Chiheb-Eddine. _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aNasraoui, Olfa. _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
776	0	8	_iPrinted edition: _z9783030074197
776	0	8	_iPrinted edition: _z9783319978635
776	0	8	_iPrinted edition: _z9783319978659
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-319-97864-2 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_aSI _cm _dz _feng _ggw _h0 _b10/2019 _ek _zSI