Charu C. Aggarwal

Outlier Analysis (eBook, PDF)

• Format: PDF

Produktbeschreibung

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 categories:

• Basic algorithms: Chapters 1 through 7 discuss the fundamental algorithms for outlier analysis, including probabilistic and statistical methods, linear methods, proximity-based methods, high-dimensional (subspace) methods, ensemble methods, and supervised methods.
• Domain-specific methods: Chapters 8 through 12 discuss outlier detection algorithms for various domains of data, such as text, categorical data, time-series data, discrete sequence data, spatial data, and network data.
• Applications: Chapter 13 is devoted to various applications of outlier analysis. Some guidance is also provided for the practitioner.

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.

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Produktdetails

• Verlag: Springer-Verlag GmbH
• Seitenzahl: 466
• Erscheinungstermin: 10. Dezember 2016
• Englisch
• ISBN-13: 9783319475783
• Artikelnr.: 53032937

Autorenporträt

Charu C. Aggarwal is a Distinguished Research Staff Member (DRSM) at the IBM T. J. Watson Research Center in Yorktown Heights, New York. He completed his undergraduate degree in Computer Science from the Indian Institute of Technology at Kanpur in 1993 and his Ph.D. in Operations Research from the Massachusetts Institute of Technology in 1996. He has published more than 300 papers in refereed conferences and journals, and has applied for or been granted more than 80 patents. He is author or editor of 15 books, including textbooks on data mining, recommender systems, and outlier analysis. Because of the commercial value of his patents, he has thrice been designated a Master Inventor at IBM. He has received several internal and external awards, including the EDBT Test-of-Time Award (2014) and the IEEE ICDM Research Contributions Award (2015). He has also served as program or general chair of many major conferences in data mining. He is a fellow of the SIAM, ACM, and the IEEE, for "contributions to knowledge discovery and data mining algorithms."

Inhaltsangabe

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.

Rezensionen

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 wantto 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)