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, andnetwork 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, neuralnet
works, outlier ensembles, time-series methods, and subspace methods. It is written as a textbook and can be used for classroom teaching.
Table of Contents
Chapter 1 An Introduction to Outlier Analysis
Chapter 2 Probabilistic and Statistical Models for Outlier Detection
Chapter 3 Linear Models for Outlier Detection
Chapter 4 Proximity-Based Outlier Detection
Chapter 5 High-Dimensional Outlier Detection: The Subspace Method
Chapter 6 Outlier Ensembles
Chapter 7 Supervised Outlier Detection
Chapter 8 Outlier Detection in Categorical, Text, and Mixed Attribute Data
Chapter 9 Time Series and Multidimensional Streaming Outlier Detection
Chapter 10 Outlier Detection in Discrete Sequences
Chapter 11 Spatial Outlier Detection
Chapter 12 Outlier Detection in Graphs andNet
Chapter 13 Applications of Outlier Analysis