Outlier Analysis

  • Charu C. Aggarwal

Table of contents

  1. Front Matter
    Pages i-xxi
  2. Charu C. Aggarwal
    Pages 1-34
  3. Charu C. Aggarwal
    Pages 65-110
  4. Charu C. Aggarwal
    Pages 111-147
  5. Charu C. Aggarwal
    Pages 185-218
  6. Charu C. Aggarwal
    Pages 219-248
  7. Charu C. Aggarwal
    Pages 311-344
  8. Charu C. Aggarwal
    Pages 345-368
  9. Charu C. Aggarwal
    Pages 369-397
  10. Charu C. Aggarwal
    Pages 399-422
  11. Back Matter
    Pages 423-465

About this book


This book provides comprehensive coverage of the field of outlier analysis from a computer science point of view. It integrates methods from data mining, machine learning, and statistics within the computational framework and therefore appeals to multiple communities. The chapters of this book can be organized into three categories:
  • Basic algorithms: Chapters 1 through 7 discuss the fundamental algorithms for outlier analysis, including probabilistic and statistical methods, linear methods, proximity-based methods, high-dimensional (subspace) methods, ensemble methods, and supervised methods.
  • Domain-specific methods: Chapters 8 through 12 discuss outlier detection algorithms for various domains of data, such as text, categorical data, time-series data, discrete sequence data, spatial data, and network data.
  • Applications: Chapter 13 is devoted to various applications of outlier analysis. Some guidance is also provided for the practitioner.

The second edition of this book is more detailed and is written to appeal to both researchers and practitioners. Significant new material has been added on topics such as kernel methods, one-class support-vector machines, matrix factorization, neural networks, outlier ensembles, time-series methods, and subspace methods. It is written as a textbook and can be used for classroom teaching. 


Outlier Analysis Anomaly detection Outlier detection Novelty detection Outlier ensembles Temporal outlier detection Temporal anomaly detection Network outlier detection Spatial outliers Streaming outlier detection Text outliers Artificial intelligence Data mining Machine learning Matrix factorization

Authors and affiliations

  • Charu C. Aggarwal
    • 1
  1. 1.IBM T.J. Watson Research CenterYorktown HeightsUSA

Bibliographic information

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