Long description:

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. Computer scientists, specifically, approach this field based on their practical experiences in managing large amounts of data, and with far fewer assumptions? the data can be of any type, structured or unstructured, and may be extremely large.

Outlier Analysis is a comprehensive exposition, as understood by data mining experts, statisticians and computer scientists. The book has been organized carefully, and emphasis was placed on simplifying the content, so that students and practitioners can also benefit. Chapters will typically cover one of three areas: methods and techniques  commonly used in outlier analysis, such as linear methods, proximity-based methods, subspace methods, and supervised methods; data  domains, such as, text, categorical, mixed-attribute, time-series, streaming, discrete sequence, spatial and network data; and key applications of these methods as applied to diverse domains such as  credit card fraud detection, intrusion detection, medical diagnosis, earth science, web log analytics, and social network analysis are covered.

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)

Table of Contents:

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.