LEADER 03186oam  2200469   450 
001 9910437575303321
005 20190911103512.0
010   $a1-4614-6396-3
024 7 $a10.1007/978-1-4614-6396-2
035   $a(OCoLC)824502708
035   $a(MiFhGG)GVRL6VDZ
035   $a(EXLCZ)992670000000341764
100   $a20121220d2013      uy             0
101 0 $aeng
135   $aurun|---uuuua
181   $ctxt
182   $cc
183   $acr
200 10$aOutlier analysis /$fCharu C. Aggarwal
205   $a1st ed. 2013.
210  1$aNew York :$cSpringer,$d2013.
215   $a1 online resource (xv, 446 pages) $cillustrations (some color)
225 0 $aGale eBooks
300   $aDescription based upon print version of record.
311   $a1-4899-8756-8 
311   $a1-4614-6395-5 
320   $aIncludes bibliographical references and index.
327   $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.
330   $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.
606   $aOutliers (Statistics)
606   $aData mining
615  0$aOutliers (Statistics)
615  0$aData mining.
676   $a519.5/2
676   $a519.52
700   $aAggarwal$b Charu C$4aut$4http://id.loc.gov/vocabulary/relators/aut$0518673
801  0$bMiFhGG
801  1$bMiFhGG
906   $aBOOK
912   $a9910437575303321
996   $aOutlier Analysis$92516027
997   $aUNINA