New Developments in Unsupervised Outlier Detection
Algorithms and Applications
1st Edition
Published on 24. November 2020
XXI, 277 pages
978-981-15-9519-6 (ISBN)
Description
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.
The authors hope the algorithms and applications proposed here will serve as valuable resources for outlier detection researchers for years to come.
More details
Series
Language
English
Place of publication
Singapore
Singapore
Publishing group
Springer Singapore
Product notice
Reflowable
Illustrations
XXI, 277 p. 138 illus., 120 illus. in color.
File size
9,95 MB
ISBN-13
978-981-15-9519-6 (9789811595196)
DOI
10.1007/978-981-15-9519-6
Schweitzer Classification
Other editions
Additional editions
Xiaochun Wang | Xiali Wang | Mitch Wilkes
New Developments in Unsupervised Outlier Detection
Algorithms and Applications
Book
11/2020
Springer
€181.89
Shipment within 3-4 weeks
Persons
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.
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.
Content
Overview 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.
