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008			150903s2013 xxu| o |||| 0|eng d
020			_a9781461463962 _99781461463962
024	7		_a10.1007/9781461463962 _2doi
035			_avtls000341904
039		9	_a201509030342 _bVLOAD _c201405050235 _dVLOAD _y201402061113 _zstaff
040			_aMX-SnUAN _bspa _cMX-SnUAN _erda
050		4	_aQA76.9.D343
100	1		_aAggarwal, Charu C. _eautor _9300405
245	1	0	_aOutlier Analysis / _cby Charu C. Aggarwal.
264		1	_aNew York, NY : _bSpringer New York : _bImprint: Springer, _c2013.
300			_axv, 446 páginas 49 ilustraciones, 10 ilustraciones en color. _brecurso en línea.
336			_atexto _btxt _2rdacontent
337			_acomputadora _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_aarchivo de texto _bPDF _2rda
500			_aSpringer eBooks
505	0		_aAn Introduction to Outlier Analysis -- Probabilistic and Statistical Models for Outlier Detection -- Linear Models for Outlier Detection -- Proximity-based Outlier Detection -- High-Dimensional Outlier Detection: The Subspace Method -- Supervised Outlier Detection -- Outlier Detection in Categorical, Text and Mixed Attribute Data -- Time Series and Multidimensional Streaming Outlier Detection -- Outlier Detection in Discrete Sequences -- Spatial Outlier Detection -- Outlier Detection in Graphs and Networks -- Applications of Outlier Analysis.
520			_aWith the increasing advances in hardware technology for data collection, and advances in software technology (databases) for data organization, computer scientists have increasingly participated in the latest advancements of the outlier analysis field. Computer scientists, specifically, approach this field based on their practical experiences in managing large amounts of data, and with far fewer assumptions– the data can be of any type, structured or unstructured, and may be extremely large. Outlier Analysis is a comprehensive exposition, as understood by data mining experts, statisticians and computer scientists. The book has been organized carefully, and emphasis was placed on simplifying the content, so that students and practitioners can also benefit. Chapters will typically cover one of three areas: methods and techniques  commonly used in outlier analysis, such as linear methods, proximity-based methods, subspace methods, and supervised methods; data  domains, such as, text, categorical, mixed-attribute, time-series, streaming, discrete sequence, spatial and network data; and key applications of these methods as applied to diverse domains such as  credit card fraud detection, intrusion detection, medical diagnosis, earth science, web log analytics, and social network analysis are covered.
590			_aPara consulta fuera de la UANL se requiere clave de acceso remoto.
710	2		_aSpringerLink (Servicio en línea) _9299170
776	0	8	_iEdición impresa: _z9781461463955
856	4	0	_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-1-4614-6396-2 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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