Outlier Analysis 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024


Outlier Analysis

简体网页||繁体网页
Charu C. Aggarwal 作者
Springer
译者
2016-12-12 出版日期
466 页数
USD 79.99 价格
Hardcover
丛书系列
9783319475776 图书编码

Outlier Analysis 在线电子书 图书标签: 异常检测  机器学习  数据分析  Outlier  outlier  计算机科学  计算机  编程   


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Outlier Analysis 在线电子书 epub 下载 mobi 下载 pdf 下载 txt 下载 2024

Outlier Analysis 在线电子书 epub 下载 mobi 下载 pdf 下载 txt 下载 2024

Outlier Analysis 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024



Outlier Analysis 在线电子书 用户评价

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项目开头基本靠此书续命,范围广但浅,往深了挖就不行

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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

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Outlier Analysis 在线电子书 著者简介

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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Outlier Analysis 在线电子书 图书目录


Outlier Analysis 在线电子书 pdf 下载 txt下载 epub 下载 mobi 在线电子书下载

Outlier Analysis 在线电子书 图书描述

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.

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