Outlier Analysis
暫譯: 異常值分析
Charu C. Aggarwal
- 出版商: Springer
- 出版日期: 2018-05-04
- 售價: $3,440
- 貴賓價: 9.5 折 $3,268
- 語言: 英文
- 頁數: 466
- 裝訂: Paperback
- ISBN: 3319837729
- ISBN-13: 9783319837727
海外代購書籍(需單獨結帳)
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提供了對異常值分析領域的全面覆蓋。它整合了數據挖掘、機器學習和統計學的方法，並在計算框架內進行，因此吸引了多個社群。本書的章節可以分為三個類別：
- 基本算法：第1至第7章討論異常值分析的基本算法，包括概率和統計方法、線性方法、基於接近度的方法、高維（子空間）方法、集成方法和監督方法。
- 特定領域的方法：第8至第12章討論針對各種數據領域的異常檢測算法，例如文本、類別數據、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章專門介紹異常值分析的各種應用。對於實務工作者也提供了一些指導。
本書的第二版更加詳細，旨在吸引研究人員和實務工作者。新增了大量重要的材料，涵蓋了如核方法、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。它被編寫為教科書，可以用於課堂教學。
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