Outlier Analysis

出版者:Springer

作者:Charu C. Aggarwal

页数:466

出版时间:2016-12-12

价格:USD 79.99

装帧:Hardcover

isbn号码:9783319475776

具体描述

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.

作者简介

From the Back Cover

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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About the Author

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 undergraduatedegree in Computer Science from the Indian Institute of Technology at Kanpur in 1993 andhis Ph.D. in Operations Research from the Massachusetts Institute of Technology in 1996.He has published more than 300 papers in refereed conferences andjournals, 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 commercialvalue of his patents, he has thrice been designated a MasterInventor at IBM. He has received several internal and externalawards, including the EDBT Test-of-Time Award (2014) andthe IEEE ICDM Research Contributions Award (2015). He has alsoserved as program or general chair of many major conferences in datamining. He is a fellow of the SIAM, ACM, and the IEEE, for “contributions to knowledgediscovery and data mining algorithms.”

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用户评价

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是一本关于异常检测的文献集，介绍了各种异常检测的算法，都是从各种文章中引用来的，但还是很不错的，对异常检测做了很详细的介绍，适合做相关研究的人阅读

是一本关于异常检测的文献集，介绍了各种异常检测的算法，都是从各种文章中引用来的，但还是很不错的，对异常检测做了很详细的介绍，适合做相关研究的人阅读

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