• library@msu.ac.th
  • Academic Resource Center Mahasarakham University

New Developments in Unsupervised Outlier Detection

" 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. "

ใส่ตะกร้า
  • ISBN9789811595189
  • ประเภท E-Book
  • ผู้แต่ง Xiaochun Wang
  • สำนักพิมพ์ Springer Nature Singapore
  • ครั้งที่พิมพ์ 1
  • ปีที่พิมพ์2021
  • ภาษาภาษาอังกฤษ
  • หมวดหมู่วิศวกรรมและการขนส่ง
: ข้อมูลหนังสือ

" 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. "