LEADER 03897nam 22005895 450 001 9910254845803321 005 20200701135055.0 010 $a3-319-47578-9 024 7 $a10.1007/978-3-319-47578-3 035 $a(CKB)3710000001006545 035 $a(DE-He213)978-3-319-47578-3 035 $a(MiAaPQ)EBC6312551 035 $a(MiAaPQ)EBC5589175 035 $a(Au-PeEL)EBL5589175 035 $a(OCoLC)1066178552 035 $a(PPN)197457886 035 $a(EXLCZ)993710000001006545 100 $a20161212d2017 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aOutlier Analysis /$fby Charu C. Aggarwal 205 $a2nd ed. 2017. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2017. 215 $a1 online resource (XXII, 466 p. 78 illus., 13 illus. in color.) 311 $a3-319-47577-0 320 $aIncludes bibliographical references and index. 327 $aAn Introduction to Outlier Analysis -- Probabilistic Models for Outlier Detection -- Linear Models for Outlier Detection -- Proximity-Based Outlier Detection -- High-Dimension Outlier Detection -- Outlier Ensembles -- Supervised Outlier Detection -- Categorical, Text, and Mixed Attribute Data -- Time Series and Streaming Outlier Detection -- Outlier Detection in Discrete Sequences -- Spatial Outlier Detection -- Outlier Detection in Graphs and Networks -- Applications of Outlier Analysis. 330 $aThis book provides comprehensive coverage of the field of outlier analysis from a computer science point of view. It integrates methods from data mining, machine learning, and statistics within the computational framework and therefore appeals to multiple communities. The chapters of this book can be organized into three categories: Basic algorithms: Chapters 1 through 7 discuss the fundamental algorithms for outlier analysis, including probabilistic and statistical methods, linear methods, proximity-based methods, high-dimensional (subspace) methods, ensemble methods, and supervised methods. Domain-specific methods: Chapters 8 through 12 discuss outlier detection algorithms for various domains of data, such as text, categorical data, time-series data, discrete sequence data, spatial data, and network data. Applications: Chapter 13 is devoted to various applications of outlier analysis. Some guidance is also provided for the practitioner. The second edition of this book is more detailed and is written to appeal to both researchers and practitioners. Significant new material has been added on topics such as kernel methods, one-class support-vector machines, matrix factorization, neural networks, outlier ensembles, time-series methods, and subspace methods. It is written as a textbook and can be used for classroom teaching. . 606 $aData mining 606 $aStatistics  606 $aArtificial intelligence 606 $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030 606 $aStatistics and Computing/Statistics Programs$3https://scigraph.springernature.com/ontologies/product-market-codes/S12008 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 615 0$aData mining. 615 0$aStatistics . 615 0$aArtificial intelligence. 615 14$aData Mining and Knowledge Discovery. 615 24$aStatistics and Computing/Statistics Programs. 615 24$aArtificial Intelligence. 676 $a006.312 700 $aAggarwal$b Charu C$4aut$4http://id.loc.gov/vocabulary/relators/aut$0518673 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910254845803321 996 $aOutlier Analysis$92516027 997 $aUNINA