Inhaltsangabe
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.
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Über die Autorin bzw. den Autor
Xiaochun Wang received her B.S. degree from Beijing University and the Ph.D. degree from the Department of Electrical Engineering and Computer Science, Vanderbilt University, the United States of America. She is currently an Associate Professor of the School of Software Engineering at Xi’an Jiaotong University. Her research interests are in computer vision, signal processing, data mining, machine learning and pattern recognition.
Xia Li Wang received his Ph.D. degree from the Department of Computer Science, Northwest University, People's Republic of China, in 2005. He is a faculty member in the School of Information Engineering, Chang’an University, China. His research interests are in computer vision, signal processing, intelligent traffic system, and pattern recognition.
D. Mitchell Wilkes received the B.S.E.E. degree from Florida Atlantic, and the M.S.E.E. and Ph.D. degrees from Georgia Institute of Technology. His researchinterests include digital signal processing, image processing and computer vision, structurally adaptive systems, sonar, as well as signal modeling. He is a member of the IEEE and a faculty member at the Department of Electrical Engineering and Computer Science, Vanderbilt University. He is a member of the IEEE.
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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.
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