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Titlebook: Interactive and Dynamic Graphics for Data Analysis; With R and GGobi Dianne Cook,Deborah F. Swayne Book 2007 Springer-Verlag New York 2007

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發(fā)表于 2025-3-21 19:19:21 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Interactive and Dynamic Graphics for Data Analysis
副標(biāo)題With R and GGobi
編輯Dianne Cook,Deborah F. Swayne
視頻videohttp://file.papertrans.cn/471/470583/470583.mp4
概述The book‘s examples use the software R and GGobi.Illustrated examples, supplemented by movies on the web, help he reader learn how to use interactive graphics for data anlaysis.Full color figures illu
叢書名稱Use R!
圖書封面Titlebook: Interactive and Dynamic Graphics for Data Analysis; With R and GGobi Dianne Cook,Deborah F. Swayne Book 2007 Springer-Verlag New York 2007
描述This book is about using interactive and dynamic plots on a computer screen as part of data exploration and modeling, both alone and as a partner with static graphics and non-graphical computational methods. The area of int- active and dynamic data visualization emerged within statistics as part of research on exploratory data analysis in the late 1960s, and it remains an active subject of research today, as its use in practice continues to grow. It now makes substantial contributions within computer science as well, as part of the growing ?elds of information visualization and data mining, especially visual data mining. The material in this book includes: ? An introduction to data visualization, explaining how it di?ers from other types of visualization. ? Adescriptionofourtoolboxofinteractiveanddynamicgraphicalmethods. ? An approach for exploring missing values in data. ? An explanation of the use of these tools in cluster analysis and supervised classi?cation. ? An overview of additional material available on the web. ? A description of the data used in the analyses and exercises. The book’s examples use the software R and GGobi. R (Ihaka & Gent- man 1996, RDevelopment CoreTeam2
出版日期Book 2007
關(guān)鍵詞classification; cluster analysis; clustering; data analysis; data analysis software; data mining; data vis
版次1
doihttps://doi.org/10.1007/978-0-387-71762-3
isbn_softcover978-0-387-71761-6
isbn_ebook978-0-387-71762-3Series ISSN 2197-5736 Series E-ISSN 2197-5744
issn_series 2197-5736
copyrightSpringer-Verlag New York 2007
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沙發(fā)
發(fā)表于 2025-3-21 23:54:39 | 只看該作者
Dianne Cook,Deborah F. SwayneThe book‘s examples use the software R and GGobi.Illustrated examples, supplemented by movies on the web, help he reader learn how to use interactive graphics for data anlaysis.Full color figures illu
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Miscellaneous Topics,er briefly, some of these different kinds of data and the additional methods needed to analyze them, beginning with a section on inference. A more complete treatment of each topic will be found on the book web site.
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978-0-387-71761-6Springer-Verlag New York 2007
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Interactive and Dynamic Graphics for Data Analysis978-0-387-71762-3Series ISSN 2197-5736 Series E-ISSN 2197-5744
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發(fā)表于 2025-3-22 23:30:53 | 只看該作者
Missing Values, For example, when measuring instruments fail, they often do so under conditions of stress, such as high temperature or humidity. As another example, a lot of the missing values in smoking cessation studies occur for those people who begin smoking again and silently withdraw from the study, perhaps out of discouragement or embarrassment.
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發(fā)表于 2025-3-23 02:55:13 | 只看該作者
Supervised Classification,ation in the header and text of each message is converted into a set of numerical variables such as the size of the email, the domain of the sender, or the presence of the word “free.” These variables are used to define rules that determine whether an incoming message is spam or ham.
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發(fā)表于 2025-3-23 06:07:23 | 只看該作者
Cluster Analysis,t can be summarized by a description of each cluster, and second, because each cluster, which is now relatively homogeneous, can be analyzed separately. Thus, it can be used to effectively reduce the size of massive amounts of data.
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