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New Developments in Unsupervised Outlier Detection

Algorithms and Applications

  • Book
  • © 2021

Overview

Authors:
  1. Xiaochun Wang

    1. School of Software Engineering, Xi’an Jiaotong University, Xi’an, China

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  2. Xiali Wang

    1. School of Information Engineering, Chang’an University, Xi’an, China

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    You can also search for this author in PubMed   Google Scholar

  3. Mitch Wilkes

    1. Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, USA

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  • 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
  • Challenges and improves on traditional ideas in outlier detection
  • Discusses an unconventional approach to multiple novel object detection applications

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Table of contents (10 chapters)

  1. Front Matter

    Pages i-xxi
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  2. Introduction

    1. Front Matter

      Pages 1-1
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    2. Overview and Contributions

      • Xiaochun Wang, Xiali Wang, Mitch Wilkes
      Pages 3-11

    3. Developments in Unsupervised Outlier Detection Research

      • Xiaochun Wang, Xiali Wang, Mitch Wilkes
      Pages 13-36

  3. New Developments in Unsupervised Outlier Detection Research

    1. Front Matter

      Pages 37-37
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    2. A Fast Distance-Based Outlier Detection Technique Using a Divisive Hierarchical Clustering Algorithm

      • Xiaochun Wang, Xiali Wang, Mitch Wilkes
      Pages 39-69

    3. A k-Nearest Neighbor Centroid-Based Outlier Detection Method

      • Xiaochun Wang, Xiali Wang, Mitch Wilkes
      Pages 71-112

    4. A Minimum Spanning Tree Clustering-Inspired Outlier Detection Technique

      • Xiaochun Wang, Xiali Wang, Mitch Wilkes
      Pages 113-145

    5. A k-Nearest Neighbour Spectral Clustering-Based Outlier Detection Technique

      • Xiaochun Wang, Xiali Wang, Mitch Wilkes
      Pages 147-172

    6. Enhancing Outlier Detection by Filtering Out Core Points and Border Points

      • Xiaochun Wang, Xiali Wang, Mitch Wilkes
      Pages 173-193

  4. Applications

    1. Front Matter

      Pages 195-195
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    2. An Effective Boundary Point Detection Algorithm Via k-Nearest Neighbors-Based Centroid

      • Xiaochun Wang, Xiali Wang, Mitch Wilkes
      Pages 197-222

    3. A Nearest Neighbor Classifier-Based Automated On-Line Novel Visual Percept Detection Method

      • Xiaochun Wang, Xiali Wang, Mitch Wilkes
      Pages 223-255

    4. Unsupervised Fraud Detection in Environmental Time Series Data

      • Xiaochun Wang, Xiali Wang, Mitch Wilkes
      Pages 257-277
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Keywords

About this book

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.



Authors and Affiliations

  • School of Software Engineering, Xi’an Jiaotong University, Xi’an, China

    Xiaochun Wang

  • School of Information Engineering, Chang’an University, Xi’an, China

    Xiali Wang

  • Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, USA

    Mitch Wilkes

About the authors

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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