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Outlier Detection: Techniques and Applications

A Data Mining Perspective

  • N. N. R. Ranga Suri
  • Narasimha Murty M
  • G. Athithan

Part of the Intelligent Systems Reference Library book series (ISRL, volume 155)

Table of contents

  1. Front Matter
    Pages i-xxii
  2. Techniques for Outlier Detection

    1. Front Matter
      Pages 1-1
    2. N. N. R. Ranga Suri, Narasimha Murty M, G. Athithan
      Pages 3-11
    3. N. N. R. Ranga Suri, Narasimha Murty M, G. Athithan
      Pages 13-27
    4. N. N. R. Ranga Suri, Narasimha Murty M, G. Athithan
      Pages 29-51
    5. N. N. R. Ranga Suri, Narasimha Murty M, G. Athithan
      Pages 53-68
    6. N. N. R. Ranga Suri, Narasimha Murty M, G. Athithan
      Pages 69-93
    7. N. N. R. Ranga Suri, Narasimha Murty M, G. Athithan
      Pages 95-111
    8. N. N. R. Ranga Suri, Narasimha Murty M, G. Athithan
      Pages 113-131
  3. Applications of Outlier Detection in Graph Data Mining

    1. Front Matter
      Pages 133-133
    2. N. N. R. Ranga Suri, Narasimha Murty M, G. Athithan
      Pages 135-158
    3. N. N. R. Ranga Suri, Narasimha Murty M, G. Athithan
      Pages 159-176
    4. N. N. R. Ranga Suri, Narasimha Murty M, G. Athithan
      Pages 177-194
    5. N. N. R. Ranga Suri, Narasimha Murty M, G. Athithan
      Pages 195-202
  4. Back Matter
    Pages 203-214

About this book

Introduction

This book, drawing on recent literature, highlights several methodologies for the detection of outliers and explains how to apply them to solve several interesting real-life problems. The detection of objects that deviate from the norm in a data set is an essential task in data mining due to its significance in many contemporary applications. More specifically, the detection of fraud in e-commerce transactions and discovering anomalies in network data have become prominent tasks, given recent developments in the field of information and communication technologies and security. Accordingly, the book sheds light on specific state-of-the-art algorithmic approaches such as the community-based analysis of networks and characterization of temporal outliers present in dynamic networks. It offers a valuable resource for young researchers working in data mining, helping them understand the technical depth of the outlier detection problem and devise innovative solutions to address related challenges.   

Keywords

Data Mining Intelligent Systems Outlier Detection Robust Statistics Rough Sets

Authors and affiliations

  • N. N. R. Ranga Suri
    • 1
  • Narasimha Murty M
    • 2
  • G. Athithan
    • 3
  1. 1.Centre for Artificial Intelligence and Robotics (CAIR)BangaloreIndia
  2. 2.Department of Computer Science and AutomationIndian Institute of Science (IISc)BangaloreIndia
  3. 3.Defence Research and Development Organization (DRDO)New DelhiIndia

Bibliographic information