作者: Ian H. Witten / Eibe Frank / Mark A.Hall
英文名: Data Mining, Fourth Edition: PracticalMachine Learning Tools and Techniques
出版社: Morgan Kaufmann
出版年: 2016-12-9
内容简介
Data Mining: Practical MachineLearning Tools and Techniques, Fourth Edition, offers a thorough grounding inmachine learning concepts, along with practical advice on applying these toolsand techniques in real-world data mining situations. This highly anticipatedfourth edition of the most acclaimed work on data mining and machine learningteaches readers everything they need to know to get going, from preparinginputs, interpreting outputs, evaluating results, to the algorithmic methods atthe heart of successful data mining approaches.
Extensive updates reflect thetechnical changes and modernizations that have taken place in the field sincethe last edition, including substantial new chapters on probabilistic methodsand on deep learning. Accompanying the book is a new version of the popularWEKA machine learning software from the University of Waikato. Authors Witten,Frank, Hall, and Pal include today's techniques coupled with the methods at theleading edge of contemporary research.
Provides a thorough grounding inmachine learning concepts, as well as practical advice on applying the toolsand techniques to data mining projectsPresents concrete tips and techniques forperformance improvement that work by transforming the input or output inmachine learning methodsIncludes a downloadable WEKA software toolkit, acomprehensive collection of machine learning algorithms for data miningtasks-in an easy-to-use interactive interfaceIncludes open-access onlinecourses that introduce practical applications of the material in the book
作者介绍
From the Back Cover
Data Mining: Practical Machine Learning Tools and Techniques offersa thorough grounding in machine learning concepts as well as practical adviceon applying the tools and techniques in real-world data mining situations. Thishighly anticipated fourth edition of the most acclaimed work on data mining andmachine learning will teach you everything you need to know to get going, frompreparing inputs, interpreting outputs, evaluating results, to the algorithmicmethods at the heart of successful data mining approaches. Extensive updatesreflect the technical changes and modernizations that have taken place in thefield since the last edition, including substantial new chapters onprobabilistic methods and on deep learning. Accompanying the book is a newversion of the popular WEKA machine learning software from the University ofWaikato. Witten, Frank, Hall and Pal include the techniques of today as well asmethods at the leading edge of contemporary research. Key Features Include:Provides a thorough grounding in machine learning concepts as well as practicaladvice on applying the tools and techniques to your data mining projectsConcrete tips and techniques for performance improvement that work bytransforming the input or output in machine learning methods Downloadable WEKAsoftware toolkit, a comprehensive collection of machine learning algorithms fordata mining tasks-in an easy-to-use interactive interface. Accompanying open-accessonline courses that introduce practical application of the material in thebook.
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About the Author
Ian H. Witten is a professor of computer science at the Universityof Waikato in New Zealand. He directs the New Zealand Digital Library researchproject. His research interests include information retrieval, machinelearning, text compression, and programming by demonstration. He received an MAin Mathematics from Cambridge University, England; an MSc in Computer Sciencefrom the University of Calgary, Canada; and a PhD in Electrical Engineeringfrom Essex University, England. He is a fellow of the ACM and of the RoyalSociety of New Zealand. He has published widely on digital libraries, machinelearning, text compression, hypertext, speech synthesis and signal processing,and computer typography. He has written several books, the latest beingManaging Gigabytes (1999) and Data Mining (2000), both from MorganKaufmann.Eibe Frank lives in New Zealand with his Samoan spouse and two lovelyboys, but originally hails from Germany, where he received his first degree incomputer science from the University of Karlsruhe. He moved to New Zealand topursue his Ph.D. in machine learning under the supervision of Ian H. Witten,and joined the Department of Computer Science at the University of Waikato as alecturer on completion of his studies. He is now an associate professor at thesame institution. As an early adopter of the Java programming language, he laidthe groundwork for the Weka software described in this book. He has contributeda number of publications on machine learning and data mining to the literatureand has refereed for many conferences and journals in these areas.>Mark A.Hall holds a bachelor’s degree in computing and mathematical sciences and aPh.D. in computer science, both from the University of Waikato. Throughout histime at Waikato, as a student and lecturer in computer science and morerecently as a software developer and data mining consultant for Pentaho, anopen-source business intelligence software company, Mark has been a corecontributor to the Weka software described in this book. He has published anumber of articles on machine learning and data mining and has refereed forconferences and journals in these areas.
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目录
Preface
PART I INTRODUCTION TO DATA MINING
CHAPTER 1 What's it all about?
1.1 Data Mining and Machine Learning
Describing Structural Patterns
Machine Learning
Data Mining
1.2 Simple Examples: The Weather Problemand Others
The Weather Problem
Contact Lenses: An Idealized Problem
Irises: A Classic Numeric Dataset
CPU Performance: Introducing NumericPrediction
Labor Negotiations: A More RealisticExample
Soybean Classification: A Classic MachineLearning Success
1.3 Fielded Applications
Web Mining
Decisions Involving Judgment
Screening Images
Load Forecasting
Diagnosis
Marketing and Sales
Other Applications
1.4The Data Mining Process
1.5 Machine Learning and Statistics
1.6 Generalization as Search
Enumerating the Concept Space
Bias
1.7 Data Mining and Ethics
Reidentification
Using Personal Information
Wider Issues
1.8 Further Reading and Bibliographic Notes
CHAPTER 2 Input: concepts, instances,attributes
CHAPTER 3 Output: knowledge representation
CHAPTER 4 Algorithms: the basic methods
CHAPTER 5 Credibility: evaluating what'sbeen learned
PART II MORE ADVANCED MACHINE LEARNINGSCHEMES
CHAPTER 6 Trees and rules
CHAPTER 7 Extending instance-based andlinear models
CHAPTER 8 Data Transformations
CHAPTER 9 Probabilistic methods
Chapter 10 Deep learning
CHAPTER 11 Beyond supervised andunsupervised learning
CHAPTER 12 Ensemble learning
CHAPTER 13 Moving on : applications andbeyond
List of Figures
List of Tables
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