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:
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.
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.
Chapter 13 is devoted to various applications of outlier analysis. Some
guidance is also provided for the practitioner.
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.