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020 _a9781461463962
_9978-1-4614-6396-2
024 7 _a10.1007/978-1-4614-6396-2
_2doi
035 _ato000483788
040 _aSpringer
_cSpringer
_dRU-ToGU
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.
_9306267
245 1 0 _aOutlier Analysis
_helectronic resource
_cby Charu C. Aggarwal.
260 _aNew York, NY :
_bSpringer New York :
_bImprint: Springer,
_c2013.
300 _aXV, 446 p. 49 illus., 10 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
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.
_9155490
650 0 _aData protection.
_9303937
650 0 _aDatabase management.
_9566224
650 0 _aData mining.
_9306371
650 0 _aInformation storage and retrieval systems.
_9137013
650 0 _aArtificial intelligence.
_9274099
650 0 _aMathematical statistics.
_9566264
650 1 4 _aComputer Science.
_9155490
650 2 4 _aData Mining and Knowledge Discovery.
_9306372
650 2 4 _aArtificial Intelligence (incl. Robotics).
_9274102
650 2 4 _aStatistics and Computing/Statistics Programs.
_9303277
650 2 4 _aSystems and Data Security.
_9303939
650 2 4 _aDatabase Management.
_9566226
650 2 4 _aInformation Storage and Retrieval.
_9303027
710 2 _aSpringerLink (Online service)
_9143950
773 0 _tSpringer eBooks
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-4614-6396-2
912 _aZDB-2-SCS
999 _c356190