Outlier Analysis

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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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出版者:Springer
作者:Charu C. Aggarwal
出品人:
頁數:466
译者:
出版時間:2016-12-12
價格:USD 79.99
裝幀:Hardcover
isbn號碼:9783319475776
叢書系列:
圖書標籤:
  • 異常檢測 
  • 機器學習 
  • 數據分析 
  • Outlier 
  • outlier 
  • 計算機科學 
  • 計算機 
  • 編程 
  •  
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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.

具體描述

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項目開頭基本靠此書續命,範圍廣但淺,往深瞭挖就不行

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讀瞭1、2、9、10章,作者把異常檢測的常用方法和思路做瞭一個綜述。

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