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