Process Mining Data Science in Action(2nd) 英文epub 第2版 本资源转载自网络，如有侵权，请联系上传者或csdn删除 查看此书详细信息请在美国亚马逊官网搜索此书

Data Mining with R Learning with Case Studies 英文无水印原版pdf pdf所有页面使用FoxitReader、PDF-XChangeViewer、SumatraPDF和Firefox测试都可以打开 本资源转载自网络，如有侵权，请联系上传者或csdn删除 查看此书详细信息请在美国亚马逊官网搜索此书

I.H.Written, E.Frank, M.Hall, C.J.Pal Highlights Explains how machine learning algorithms for data mining work. Helps you compare and evaluate the results of different techniques. Covers performance improvement techniques, including input preprocessing and combining output from different methods. Features in-depth information on probabilistic models and deep learning. Provides an introduction to the Weka machine learning workbench and links to algorithm implementations in the software.

* Packed with more than forty percent new and updated material, this edition shows business managers, marketing analysts, and data mining specialists how to harness fundamental data mining methods and techniques to solve common types of business problems 　　 * Each chapter covers a new data mining technique, and then shows readers how to apply the technique for improved marketing, sales, and customer support 　　 * The authors build on their reputation for concise, clear, and practical explanations of complex concepts, making this book the perfect introduction to data mining 　　 * More advanced chapters cover such topics as how to prepare data for analysis and how to create the necessary infrastructure for data mining 　　 * Covers core data mining techniques, including decision trees, neural networks, collaborative filtering, association rules, link analysis, clustering, and survival analysis

这本书可是英美经典书籍。 What you need to know about data mining and data-analytic thinking

Mining of Massive Dataset的中文版

作者为Jiawei Han 和 Micheline Kamber，数据挖掘领域经典之作，英文第二版，大部分高校的数据挖掘课的教材。此为其课后习题答案。

Data Mining: Concepts and Techniques 3rd第三版原文 带书签Data Mining: Concepts and Techniques 3rdData Mining: Concepts and Techniques 3rdData Mining: Concepts and Techniques 3rd

Graph mining and management has become an important topic of research re- cently because of numerous applications to a wide variety of data mining prob- lems in computational biology, chemical data analysis, drug discovery and com- munication networking. Traditional data mining and management algorithms such as clustering, classification, frequent pattern mining and indexing have now been extended to the graph scenario. This book contains a number of chapters which are carefully chosen in order to discuss the broad research issues in graph management and mining. In addition, a number of important applications of graph mining are also covered in the book. The purpose of this chapter is to provide an overview of the different kinds of graph processing and mining tech- niques, and the coverage of these topics in this book.

David J. Hand Department of Mathematics, Imperial College London, London, UK Data mining is the discovery of interesting, unexpected or valuable structures in large datasets. As such, it has two rather different aspects. One of these concerns large-scale, ‘global’ structures, and the aim is to model the shapes, or features of the shapes, of distributions.