| 书目名称 | New Developments in Unsupervised Outlier Detection |
| 副标题 | Algorithms and Appli |
| 编辑 | Xiaochun Wang,Xiali Wang,Mitch Wilkes |
| 视频video | http://file.papertrans.cn/666/665046/665046.mp4 |
| 概述 | Presents algorithms for unsupervised outlier detection using k-nearest neighbor-based methods.Proposes new global and local outlier factors that offer performance comparable to existing solutions.Chal |
| 图书封面 |  |
| 描述 | This 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.. |
| 出版日期 | Book 2021 |
| 关键词 | Unsupervised Outlier Detection; Distance-Based Outlier Detection; Density-Based Outlier Detection; k-Ne |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-981-15-9519-6 |
| isbn_softcover | 978-981-15-9521-9 |
| isbn_ebook | 978-981-15-9519-6 |
| copyright | Xi‘an Jiaotong University Press 2021 |
|Archiver|手机版|小黑屋|
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