Outlier Analysis

1. Front Matter

   Pages i-xxi

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2. An Introduction to Outlier Analysis

   • Charu C. Aggarwal

   Pages 1-34

3. Probabilistic and Statistical Models for Outlier Detection

   • Charu C. Aggarwal

   Pages 35-64

4. Linear Models for Outlier Detection

   • Charu C. Aggarwal

   Pages 65-110

5. Proximity-Based Outlier Detection

   • Charu C. Aggarwal

   Pages 111-147

6. High-Dimensional Outlier Detection: The Subspace Method

   • Charu C. Aggarwal

   Pages 149-184

7. Outlier Ensembles

   • Charu C. Aggarwal

   Pages 185-218

8. Supervised Outlier Detection

   • Charu C. Aggarwal

   Pages 219-248

9. Outlier Detection in Categorical, Text, and Mixed Attribute Data

   • Charu C. Aggarwal

   Pages 249-272

10. Time Series and Multidimensional Streaming Outlier Detection

    • Charu C. Aggarwal

    Pages 273-310

11. Outlier Detection in Discrete Sequences

    • Charu C. Aggarwal

    Pages 311-344

12. Spatial Outlier Detection

    • Charu C. Aggarwal

    Pages 345-368

13. Outlier Detection in Graphs and Networks

    • Charu C. Aggarwal

    Pages 369-397

14. Applications of Outlier Analysis

    • Charu C. Aggarwal

    Pages 399-422

15. Back Matter

    Pages 423-465

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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

“This book presents an extensive coverage on outlier analysis from data mining and computer science perspective. Each chapter includes a detailed coverage of the topics, case studies, extensive bibliographic notes, a number of exercises, and the future direction of research in this field. The book is a good source for researchers also could be used as textbook in the related discipline.” (Morteza Marzjarani, Technometrics, Vol. 60 (2), 2018)​

• IBM T.J. Watson Research Center, Yorktown Heights, USA

  Charu C. Aggarwal

Charu C. Aggarwal is a Distinguished Research Staff Member (DRSM) at the IBM T. J. Watson Research Center in Yorktown Heights, New York. He completed his undergraduate degree in Computer Science from the Indian Institute of Technology at Kanpur in 1993 and his Ph.D. in Operations Research from the Massachusetts Institute of Technology in 1996. He has published more than 300 papers in refereed conferences and journals, and has applied for or been granted more than 80 patents. He is author or editor of 15 books, including textbooks on data mining, recommender systems, and outlier analysis. Because of the commercial value of his patents, he has thrice been designated a Master Inventor at IBM. He has received several internal and external awards, including the EDBT Test-of-Time Award (2014) and the IEEE ICDM Research Contributions Award (2015). He has also served as program or general chair of many major conferences in data mining. He is a fellow of the SIAM, ACM, and the IEEE, for “contributions to knowledge discovery and data mining algorithms.”

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