LEADER 03079oam 2200481 450 001 9910483597703321 005 20210512084630.0 010 $a981-15-9519-4 024 7 $a10.1007/978-981-15-9519-6 035 $a(CKB)4100000011610236 035 $a(MiAaPQ)EBC6408092 035 $a(DE-He213)978-981-15-9519-6 035 $a(PPN)252505522 035 $a(EXLCZ)994100000011610236 100 $a20210512d2021 uy 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aNew developments in unsupervised outlier detection $ealgorithms and applications /$fXiaochun Wang, Xiali Wang, Mitch Wilkes 205 $a1st ed. 2021. 210 1$aSingapore :$cSpringer,$d[2021] 210 4$d©2021 215 $a1 online resource (XXI, 277 p. 138 illus., 120 illus. in color.) 311 $a981-15-9518-6 327 $aOverview and Contributions -- Developments in Unsupervised Outlier Detection Research -- A Fast Distance-Based Outlier Detection Technique Using A Divisive Hierarchical Clustering Algorithm -- A k-Nearest Neighbour Centroid Based Outlier Detection Method -- A Minimum Spanning Tree Clustering Inspired Outlier Detection Technique -- A k-Nearest Neighbour Spectral Clustering Based Outlier Detection Technique -- Enhancing Outlier Detection by Filtering Out Core Points and Border Points -- An Effective Boundary Point Detection Algorithm via k-Nearest Neighbours Based Centroid -- A Nearest Neighbour Classifier Based Automated On-Line Novel Visual Percept Detection Method -- Unsupervised Fraud Detection in Environmental Time Series Data. . 330 $aThis book enriches unsupervised outlier detection research by proposing several new distance-based and density-based outlier scores in a k-nearest neighbors? setting. The respective chapters highlight the latest developments in k-nearest neighbor-based outlier detection research and cover such topics as our present understanding of unsupervised outlier detection in general; distance-based and density-based outlier detection in particular; and the applications of the latest findings to boundary point detection and novel object detection. The book also offers a new perspective on bridging the gap between k-nearest neighbor-based outlier detection and clustering-based outlier detection, laying the groundwork for future advances in unsupervised outlier detection research. The authors hope the algorithms and applications proposed here will serve as valuable resources for outlier detection researchers for years to come. 606 $aData mining 606 $aOutliers (Statistics) 615 0$aData mining. 615 0$aOutliers (Statistics) 676 $a006.312 700 $aWang$b Xiaochun$01225159 702 $aWang$b Xiali 702 $aWilkes$b Mitch 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bUtOrBLW 906 $aBOOK 912 $a9910483597703321 996 $aNew developments in unsupervised outlier detection$92844686 997 $aUNINA LEADER 01584nam 22004573 450 001 9910163920103321 005 20250605080351.0 010 $a1-62517-322-9 035 $a(CKB)3710000001055935 035 $a(BIP)010276158 035 $a(VLeBooks)9781625173225 035 $a(MiAaPQ)EBC32132414 035 $a(Au-PeEL)EBL32132414 035 $a(OCoLC)1152317075 035 $a(EXLCZ)993710000001055935 100 $a20250605d2004 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aDeath by Dissertation 205 $a1st ed. 210 1$aNEW YORK :$cNancy Yost Literary Agency,$d2004. 210 4$d©2004. 215 $a1 online resource (201 p.) 225 1 $aDeep South Mysteries Series ;$vv.3 311 08$a1-57072-266-8 330 8 $aFor Andy Carpenter, graduate school is stressful enough without adding romantic complications and dead bodies to the mix. When Andy discovers the body of fellow graduate student Charlie Harper in the university library, Rob Hayward, a new student, looks like the chief suspect in the murder. Just what was the relationship between Rob and Charlie? 410 0$aDeep South Mysteries Series 610 $aFiction 610 $aCarpenter, andy (fictitious character) 610 $aHouston (tex.) 700 $aJames$b Dean$01433016 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910163920103321 996 $aDeath by Dissertation$93578199 997 $aUNINA