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001			sulb-eb0022534
003			BD-SySUS
005			20160413122304.0
007			cr nn 008mamaa
008			130109s2013 xxu| s |||| 0|eng d
020			_a9781461463962 _9978-1-4614-6396-2
024	7		_a10.1007/978-1-4614-6396-2 _2doi
050		4	_aQA76.9.D343
072		7	_aUNF _2bicssc
072		7	_aUYQE _2bicssc
072		7	_aCOM021030 _2bisacsh
082	0	4	_a006.312 _223
100	1		_aAggarwal, Charu C. _eauthor.
245	1	0	_aOutlier Analysis _h[electronic resource] / _cby Charu C. Aggarwal.
264		1	_aNew York, NY : _bSpringer New York : _bImprint: Springer, _c2013.
300			_aXV, 446 p. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
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.
650		0	_aComputer science.
650		0	_aComputer security.
650		0	_aDatabase management.
650		0	_aData mining.
650		0	_aInformation storage and retrieval.
650		0	_aArtificial intelligence.
650		0	_aStatistics.
650	1	4	_aComputer Science.
650	2	4	_aData Mining and Knowledge Discovery.
650	2	4	_aArtificial Intelligence (incl. Robotics).
650	2	4	_aStatistics and Computing/Statistics Programs.
650	2	4	_aSystems and Data Security.
650	2	4	_aDatabase Management.
650	2	4	_aInformation Storage and Retrieval.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9781461463955
856	4	0	_uhttp://dx.doi.org/10.1007/978-1-4614-6396-2
912			_aZDB-2-SCS
942			_2Dewey Decimal Classification _ceBooks
999			_c44626 _d44626