New Book: Outlier Analysis, by Charu Aggarwal
A first comprehensive text book in this area from a data mining and computer science perspective. Each chapter contains carefully organized content on the topic, case studies, extensive bibliographic notes and the future direction of research in this field.
BOOK ANNOUNCEMENT: Outlier Analysis
Author: Charu Aggarwal
Springer, January 2013
Approximately 440 pages
This is an authored text book on outlier analysis. The book can be considered a first comprehensive text book in this area from a data mining and computer science perspective. Most of the earlier books in outlier detection were written from a statistical perspective, and precede the emergence of the data mining field over the last 15-20 years.
Each chapter contains carefully organized content on the topic, case studies, extensive bibliographic notes and the future direction of research in this field. Thus, the book can also be used as a reference aid. Emphasis was placed on simplifying the content, so that the material is relatively easy to assimilate. The book assumes relatively little prior background, other than a very basic understanding of probability and statistical concepts. Therefore, in spite of its deep coverage, it can also provide a good introduction to the beginner. The book includes exercises as well, so that it can be used as a teaching aid.
Chapters cover one of three areas:
(i) methods and techniques commonly used in outlier analysis, such as linear methods, proximity-based methods, subspace methods, and supervised methods;
(ii) data domains, such as, text, categorical, mixed-attribute, time-series, streaming, discrete sequence, spatial and network data;
(iii) 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.
The table of contents and introductory chapter may be found at:
Amazon page for Outlier Analysis, by Charu Aggarwal.