Outlier Analysis

Charu C. Aggarwal

Outlier Analysis

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商品描述

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. 

 

商品描述(中文翻譯)

這本書從計算機科學的角度全面介紹了異常值分析領域。它在計算框架中整合了數據挖掘、機器學習和統計方法，因此適用於多個社群。本書的章節可以分為三個類別：

基本算法：第1章到第7章討論了異常值分析的基本算法，包括概率和統計方法、線性方法、基於相似度的方法、高維（子空間）方法、集成方法和監督方法。

特定領域方法：第8章到第12章討論了各種數據領域的異常值檢測算法，例如文本、分類數據、時間序列數據、離散序列數據、空間數據和網絡數據。

應用：第13章專門介紹了異常值分析的各種應用。同時也提供了一些實踐指南。

這本書的第二版更加詳細，旨在吸引研究人員和實踐者。在核方法、單類支持向量機、矩陣分解、神經網絡、異常值集成、時間序列方法和子空間方法等主題上增加了重要的新材料。它是一本教科書，可用於課堂教學。

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