With the increasing advances in hardware technology for data collection, and advances in software technology (databases) for data organization, computer scientists have increasingly participated in the latest advancements of the outlier analysis field.

An Introduction to Outlier Analysis.- Probabilistic and Statistical Models for Outlier Detection.- Linear Models for Outlier Detection.- Proximity-based Outlier Detection.- High-Dimensional Outlier Detection: The Subspace Method.- Supervised Outlier Detection.- Outlier Detection in Categorical, Text and Mixed Attribute Data.- Time Series and Multidimensional Streaming Outlier Detection.- Outlier Detection in Discrete Sequences.- Spatial Outlier Detection.- Outlier Detection in Graphs and Networks.- Applications of Outlier Analysis.

From the book reviews:“Aggarwal has written a complete survey of the state of the art in anomaly detection. … His book provides a solid frame of reference for those interested in anomaly detection, both researchers and practitioners, no matter whether they are generalists or they are mostly focused on particular applications. All of them can benefit from the broad overview of the field, the nice introductions to many different techniques, and the annotated pointers for further reading that this book provides.” (Fernando Berzal, Computing Reviews, August, 2014)“This book is an encyclopedia of how to handle outliers. The author introduces various methods to deal with outliers under various conditions, but in a systematic way so that one can easily find what one needs. The writing style is accessible to readers who do not have deep statistical training. … a good reference book for practitioners and researchers who are not experts in outlier analysis, but want to gain a basic understanding of how to do it.” (Hung Hung, Mathematical Reviews, March, 2014)“This book aims at providing a missing formal view of recent advances in outlier analysis that have been carried out mostly independently in both the computer science and statistics communities. … the book contains a series of carefully created exercises, attempting to make the book useful as a textbook. … All in all, this is an excellent book. … the book seems to be oriented more towards the experienced researcher who will use this book as reference material … .” (Santiago Ontanon, zbMATH, Vol. 1291, 2014)

Each chapter contains key research content on the topic, case studies, extensive bibliographic notes and the future direction of research in this field Covers applications for credit card fraud, network intrusion detection, law enforcement and more Content is simplified so students and practitioners can also benefit from this book Includes supplementary material: sn.pub/extras