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Outlier Analysis by Charu C. Aggarwal
Publisher: Springer; 2013 edition (January 11, 2013)
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¨C 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)
Charu Aggarwal is a Distinguished Research Staff Member at the IBM T. J. Watson Research Center in Yorktown Heights, New York. He completed his B.S. from IIT Kanpur in 1993 and his Ph.D. from Massachusetts Institute of Technology in 1996. His research interest during his Ph.D. years was in combinatorial optimization (network flow algorithms), and his thesis advisor was Professor James B. Orlin . He has since worked in the field of performance analysis, databases, and data mining. He has published over 200 papers in refereed conferences and journals, and has applied for or been granted over 80 patents. Because of the commercial value of the aforementioned patents, he has received several invention achievement awards and has thrice been designated a Master Inventor at IBM. He is a recipient of an IBM Corporate Award (2003) for his work on bio-terrorist threat detection in data streams, a recipient of the IBM Outstanding Innovation Award (2008) for his scientific contributions to privacy technology, and a recipient of an IBM Research Division Award (2008) for his scientific contributions to data stream research. He received the EDBT 2014 test-of-time award for his work on condensation-based privacy-preserving data mining. He has served on the program committees of most major database/data mining conferences, and served as program vice-chairs of the WWW Conference 2009. He served as an associate editor of the IEEE Transactions on Knowledge and Data Engineering from 2004 to 2008. He is an associate editor of the ACM Transactions on Data Mining and Knowledge Discovery, an action editor of the Data Mining and Knowledge Discovery Journal , an associate editor of the ACM SIGKDD Explorations, and an associate editor of the Knowledge and Information Systems Journal. He received the EDBT 2014 test of time award for his work on condensation based privacy. He is a fellow of the ACM (2013) and the IEEE (2010) for "contributions to knowledge discovery and data mining".