数据挖掘入门书:Roiger&Geatz《数据挖掘教程》清华版2003——从我那Windows Live Spaces转过来,表示决裂

本文介绍了Richard J. Roiger和Michael W. Geatz合著的数据挖掘教程,该书由翁敬农翻译,清华大学出版社出版。书中附带数据、讲义和iDA学生版软件。内容涵盖数据挖掘策略、技术及iDA应用,适合入门学习。同时,提供了作者主页、出版社主页和iDA软件的相关资源链接。

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这个教材推荐转自我原来在 Windows Live Spaces的博,链接也不给了,一会删掉它:
数据挖掘入门书:Roiger&Geatz《数据挖掘教程》清华版2003

开学用来入门的数据挖掘教科书,包括完整的数据挖掘流程的介绍、大多数流行数据挖掘技术的展示以及一个基于EXcel的数据挖掘工具iDA的详细展示,其实它的英文名能突出它的入门性质:

Data Mining A Tutorial-Based Primer, by Richard J. Roiger, Michael W. Geatz, Addison-Wesley,2003
《数据挖掘教程》,翁敬农译,清华大学出版社,2003,45.00元(附光盘一张,包括书中的数据、讲义以及iDA学生版)

更多的资料,

—在作者的主页上有这本书更多的数据集、讲义、订正、iDA下载以及额外的在DOS下运行的小软件Decision Tree和Association Rule,一个是做决策树,另一个做关联分析。

这本书在出版社的主页,有这本书的两个样章:第二章关于数据挖掘策略及相关技术的讨论,第四章是关于iDA应用的详细介绍,感兴趣可以对照目录了解一下这书。

iDA软件的主页,也可以下载它的限制版,应付本书的大多例子没问题。这里还有该软件的安装使用说明。诡异的是,开学初我下过能使,自从重装系统后就没法用了。现在我改用其他,也就不在意了。当然,那东西作为Excel的一个小插件出现,非常好使。

其实学东西,现在我的感受是,永远不是没有资料,缺的只是扎进一本书里。

Data Mining: The Textbook By 作者: Charu C. Aggarwal ISBN-10 : 3319141414 ISBN-13 : 9783319141411 Edition : 2015 出版日期: 2015-04-14 pages 页数: (734 ) $89.99 This textbook explores the different aspects of data mining from the fundamentals to the complex data types and their applications, capturing the wide diversity of problem domains for data mining issues. It goes beyond the traditional focus on data mining problems to introduce advanced data types such as text, time series, discrete sequences, spatial data, graph data, and social networks. Until now, no single book has addressed all these topics in a comprehensive and integrated way. The chapters of this book fall into one of three categories: Fundamental chapters: Data mining has four main problems, which correspond to clustering, classification, association pattern mining, and outlier analysis. These chapters comprehensively discuss a wide variety of methods for these problems. Domain chapters: These chapters discuss the specific methods used for different domains of data such as text data, time-series data, sequence data, graph data, and spatial data. Application chapters: These chapters study important applications such as stream mining, Web mining, ranking, recommendations, social networks, and privacy preservation. The domain chapters also have an applied flavor. Appropriate for both introductory and advanced data mining courses, Data Mining: The Textbook balances mathematical details and intuition. It contains the necessary mathematical details for professors and researchers, but it is presented in a simple and intuitive style to improve accessibility for students and industrial practitioners (including those with a limited mathematical background). Numerous illustrations, examples, and exercises are included, with an emphasis on semantically interpretable examples.
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