000			04084cam a2200421Ii 4500
999			_c95645 _d95645 _x1
001			95645
003			ES-MaUEC
005			20240111050134.0
006			m o d
007			cr cnu|||unuuu
008			170324s2017 sz ob 001 0 eng d
020			_a3319534742 _q(electronic bk.)
020			_a9783319534749 _q(electronic bk.)
020			_z3319534734
020			_z9783319534732
040			_aN$T _cN$T _dN$T _dYDX _dGW5XE _dNJR _dUAB _dCCO _dOCLCF _dIOG _dCOO _dAZU _dUPM _dESU _dJBG _dIAD _dICW _dICN _dOTZ _dOCLCQ _dU3W _dES-MaUEC _bspa
050		4	_aQ342 _b.D383 2017 EB
245	0	0	_aData science and big data : _ban environment of computational intelligence _cWitold Pedrycz, Shyi-Ming Chen, editors.
264		1	_aCham _bSpringer _c[2017]
264		4	_c2017
300			_a1 recurso en línea (viii, 303 páginas)
336			_aTexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	0		_aStudies in big data _vv. 24
500			_aSpringerLink
504			_aIncluye referencias bibliográficas e índice
505	0		_aPart I. Fundamentals -- Large-Scale Clustering Algorithms -- On High Dimensional Search Space and Learning Methods.-Enhanced Over_Sampling Techniques for Imbalanced Big Data Set Classification -- Online Anomaly Detection in Big Data: The First Line of Defense Against Intruders -- Developing Modified Classifier for Big Data Paradigm: An Approach through Bio-Inspired Soft Computing -- Unified Framework for Control of Machine Learning Tasks Towards Effective and Efficient Processing of Big Data -- An Efficient Approach for Mining High Utility Itemsets over Data Streams -- Event Detection in Location-Based Social Networks -- Part II. Applications -- Using Computational Intelligence for the Safety Assessment of Oil and Gas Pipelines: A Survey -- Big Data for Effective Management of Smart Grids -- Distributed Machine Learning on Smart-Gateway Network Towards Real-Time Indoor Data Analytics -- Predicting Spatiotemporal Impacts of Weather on Power Systems using Big Data Science -- Index.
520	3		_aThis book presents a comprehensive and up-to-date treatise of a range of methodological and algorithmic issues. It also discusses implementations and case studies, identifies the best design practices, and assesses data analytics business models and practices in industry, health care, administration and business. Data science and big data go hand in hand and constitute a rapidly growing area of research and have attracted the attention of industry and business alike. The area itself has opened up promising new directions of fundamental and applied research and has led to interesting applications, especially those addressing the immediate need to deal with large repositories of data and building tangible, user-centric models of relationships in data. Data is the lifeblood of today's knowledge-driven economy. Numerous data science models are oriented towards end users and along with the regular requirements for accuracy (which are present in any modeling), come the requirements for ability to process huge and varying data sets as well as robustness, interpretability, and simplicity (transparency). Computational intelligence with its underlying methodologies and tools helps address data analytics needs. The book is of interest to those researchers and practitioners involved in data science, Internet engineering, computational intelligence, management, operations research, and knowledge-based systems.
988			_aEBOOK, asignarmaterias, EBSPRINGER_2017C
650		7	_aInteligencia artificial _2embne _0(OCoLC)fst00871995 _0 _9413115
700	1		_aChen, Shyi-Ming _eeditor literario _998849
700	1		_aPedrycz, Witold _d1953- _eeditor literario _967495
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=http://link.springer.com/10.1007/978-3-319-53474-9 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cM15
998			_b02/2018 _dz _e- _zSI