Outlier Analysis

by Charu C. Aggarwal

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 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.

Genres: Mathematics, Science

464 pages, Paperback

First published January 1, 2013

Reviews

[Terran M · Rating 5 out of 5]

This is an excellent, clearly-written book on outlier analysis and its cousin, anomaly detection. In my opinion the author uses exactly the right amount of equations, coupled with good prose, to clearly convey the concepts and provide a coherent framework to attach them to. If you liked Introduction to Statistical Learning by Jame et al, I think you will also like this book.

Note that although it is well written, this is not an introductory book. To decide if you are ready for it, I recommend downloading and reading the first chapter from the author's website. Look at the exercises at the end; if you can do them easily, you're ready for this book. If you have any difficulty at all, go read or review Hastie and Tibshirani first and then come back.

[Vinicius Catelani · Rating 5 out of 5]

It is an amazing book to learn more about outlier analysis and detection, it is an intuitive book, it doesn't have much math but it uses the right examples and amount of math necessary to understand each possible application of outlier analysis and detection. However, the book is not for beginners, it is necessary a previous, but not so deep, knowledge of linear algebra and calculus so you can follow the explanations with ease.
Two books I recommend reading before starting this one are "APEX Calculus", by Gregory Hartman, and "Mathematics for Machine Learning", by Marc Peter Deisenroth, they both will give you enough basis to follow all the explanations about outlier analysis and detection.

[Raoul · Rating 4 out of 5]

Clear and comprehensive. The section on neural networks could be expanded.