Data Mining - 4th Edition - ISBN: 9780128042915, 9780128043578

Data Mining

4th Edition

Practical Machine Learning Tools and Techniques

Authors: Ian Witten Eibe Frank Mark Hall Christopher Pal
eBook ISBN: 9780128043578
Paperback ISBN: 9780128042915
Imprint: Morgan Kaufmann
Published Date: 17th November 2016
Page Count: 654
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Description

Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches.

Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including substantial new chapters on probabilistic methods and on deep learning. Accompanying the book is a new version of the popular WEKA machine learning software from the University of Waikato. Authors Witten, Frank, Hall, and Pal include today's techniques coupled with the methods at the leading edge of contemporary research.

Please visit the book companion website at  http://www.cs.waikato.ac.nz/ml/weka/book.html

It contains

  • Powerpoint slides for Chapters 1-12. This is a very comprehensive teaching resource, with many PPT slides covering each chapter of the book
  • Online Appendix on the Weka workbench; again a very comprehensive learning aid for the open source software that goes with the book
  • Table of contents, highlighting the many new sections in the 4th edition, along with reviews of the 1st edition, errata, etc.

 

Key Features

  • Provides a thorough grounding in machine learning concepts, as well as practical advice on applying the tools and techniques to data mining projects
  • Presents concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods
  • Includes a downloadable WEKA software toolkit, a comprehensive collection of machine learning algorithms for data mining tasks-in an easy-to-use interactive interface
  • Includes open-access online courses that introduce practical applications of the material in the book

Readership

Data analysts, data scientists, data architects. Business analysts, computer science students taking courses in data mining and machine learning

Table of Contents

Part I: Introduction to data mining

Chapter 1. What’s it all about?

  • Abstract
  • 1.1 Data Mining and Machine Learning
  • 1.2 Simple Examples: The Weather Problem and Others
  • 1.3 Fielded Applications
  • 1.4 The Data Mining Process
  • 1.5 Machine Learning and Statistics
  • 1.6 Generalization as Search
  • 1.7 Data Mining and Ethics
  • 1.8 Further Reading and Bibliographic Notes

Chapter 2. Input: Concepts, instances, attributes

  • Abstract
  • 2.1 What’s a Concept?
  • 2.2 What’s in an Example?
  • 2.3 What’s in an Attribute?
  • 2.4 Preparing the Input
  • 2.5 Further Reading and Bibliographic Notes

Chapter 3. Output: Knowledge representation

  • Abstract
  • 3.1 Tables
  • 3.2 Linear Models
  • 3.3 Trees
  • 3.4 Rules
  • 3.5 Instance-Based Representation
  • 3.6 Clusters
  • 3.7 Further Reading and Bibliographic Notes

Chapter 4. Algorithms: The basic methods

  • Abstracts
  • 4.1 Inferring Rudimentary Rules
  • 4.2 Simple Probabilistic Modeling
  • 4.3 Divide-and-Conquer: Constructing Decision Trees
  • 4.4 Covering Algorithms: Constructing Rules
  • 4.5 Mining Association Rules
  • 4.6 Linear Models
  • 4.7 Instance-Based Learning
  • 4.8 Clustering
  • 4.9 Multi-instance Learning
  • 4.10 Further Reading and Bibliographic Notes
  • 4.11 Weka Implementations

Chapter 5. Credibility: Evaluating what’s been learned

  • Abstract
  • 5.1 Training and Testing
  • 5.2 Predicting Performance
  • 5.3 Cross-Validation
  • 5.4 Other Estimates
  • 5.5 Hyperparameter Selection
  • 5.6 Comparing Data Mining Schemes
  • 5.7 Predicting Probabilities
  • 5.8 Counting the Cost
  • 5.9 Evaluating Numeric Prediction
  • 5.10 The MDL Principle
  • 5.11 Applying the MDL Principle to Clustering
  • 5.12 Using a Validation Set for Model Selection
  • 5.13 Further Reading and Bibliographic Notes

Part II: More advanced machine learning schemes

Chapter 6. Trees and rules

  • Abstract
  • 6.1 Decision Trees
  • 6.2 Classification Rules
  • 6.3 Association Rules
  • 6.4 Weka Implementations

Chapter 7. Extending instance-based and linear models

  • Abstract
  • 7.1 Instance-Based Learning
  • 7.2 Extending Linear Models
  • 7.3 Numeric Prediction With Local Linear Models
  • 7.4 Weka Implementations

Chapter 8. Data transformations

  • Abstracts
  • 8.1 Attribute Selection
  • 8.2 Discretizing Numeric Attributes
  • 8.3 Projections
  • 8.4 Sampling
  • 8.5 Cleansing
  • 8.6 Transforming Multiple Classes to Binary Ones
  • 8.7 Calibrating Class Probabilities
  • 8.8 Further Reading and Bibliographic Notes
  • 8.9 Weka Implementations

Chapter 9. Probabilistic methods

  • Abstract
  • 9.1 Foundations
  • 9.2 Bayesian Networks
  • 9.3 Clustering and Probability Density Estimation
  • 9.4 Hidden Variable Models
  • 9.5 Bayesian Estimation and Prediction
  • 9.6 Graphical Models and Factor Graphs
  • 9.7 Conditional Probability Models
  • 9.8 Sequential and Temporal Models
  • 9.9 Further Reading and Bibliographic Notes
  • 9.10 Weka Implementations

Chapter 10. Deep learning

  • Abstract
  • 10.1 Deep Feedforward Networks
  • 10.2 Training and Evaluating Deep Networks
  • 10.3 Convolutional Neural Networks
  • 10.4 Autoencoders
  • 10.5 Stochastic Deep Networks
  • 10.6 Recurrent Neural Networks
  • 10.7 Further Reading and Bibliographic Notes
  • 10.8 Deep Learning Software and Network Implementations
  • 10.9 WEKA Implementations

Chapter 11. Beyond supervised and unsupervised learning

  • Abstract
  • 11.1 Semisupervised Learning
  • 11.2 Multi-instance Learning
  • 11.3 Further Reading and Bibliographic Notes
  • 11.4 WEKA Implementations

Chapter 12. Ensemble learning

  • Abstract
  • 12.1 Combining Multiple Models
  • 12.2 Bagging
  • 12.3 Randomization
  • 12.4 Boosting
  • 12.5 Additive Regression
  • 12.6 Interpretable Ensembles
  • 12.7 Stacking
  • 12.8 Further Reading and Bibliographic Notes
  • 12.9 WEKA Implementations

Chapter 13. Moving on: applications and beyond

  • Abstract
  • 13.1 Applying Machine Learning
  • 13.2 Learning From Massive Datasets
  • 13.3 Data Stream Learning
  • 13.4 Incorporating Domain Knowledge
  • 13.5 Text Mining
  • 13.6 Web Mining
  • 13.7 Images and Speech
  • 13.8 Adversarial Situations
  • 13.9 Ubiquitous Data Mining
  • 13.10 Further Reading and Bibliographic Notes
  • 13.11 WEKA Implementations

Appendix A. Theoretical foundations

  • A.1 Matrix Algebra
  • A.2 Fundamental Elements of Probabilistic Methods

Appendix B. The WEKA workbench

  • B.1 What’s in WEKA?
  • B.2 The package management system
  • B.3 The Explorer
  • B.4 The Knowledge Flow Interface
  • B.5 The Experimenter

Details

No. of pages:
654
Language:
English
Copyright:
© Morgan Kaufmann 2017
Published:
Imprint:
Morgan Kaufmann
eBook ISBN:
9780128043578
Paperback ISBN:
9780128042915

About the Author

Ian Witten

Ian H. Witten is a professor of computer science at the University of Waikato in New Zealand. He directs the New Zealand Digital Library research project. His research interests include information retrieval, machine learning, text compression, and programming by demonstration. He received an MA in Mathematics from Cambridge University, England; an MSc in Computer Science from the University of Calgary, Canada; and a PhD in Electrical Engineering from Essex University, England. He is a fellow of the ACM and of the Royal Society of New Zealand. He has published widely on digital libraries, machine learning, text compression, hypertext, speech synthesis and signal processing, and computer typography. He has written several books, the latest being Managing Gigabytes (1999) and Data Mining (2000), both from Morgan Kaufmann.

Affiliations and Expertise

Professor, Computer Science Department, University of Waikato, New Zealand.

Eibe Frank

Eibe Frank lives in New Zealand with his Samoan spouse and two lovely boys, but originally hails from Germany, where he received his first degree in computer science from the University of Karlsruhe. He moved to New Zealand to pursue 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 a lecturer on completion of his studies. He is now an associate professor at the same institution. As an early adopter of the Java programming language, he laid the groundwork for the Weka software described in this book. He has contributed a number of publications on machine learning and data mining to the literature and has refereed for many conferences and journals in these areas.>

Affiliations and Expertise

Associate Professor, Department of Computer Science, University of Waikato, Hamilton, New Zealand

Mark Hall

Mark A. Hall holds a bachelor’s degree in computing and mathematical sciences and a Ph.D. in computer science, both from the University of Waikato. Throughout his time at Waikato, as a student and lecturer in computer science and more recently as a software developer and data mining consultant for Pentaho, an open-source business intelligence software company, Mark has been a core contributor to the Weka software described in this book. He has published a number of articles on machine learning and data mining and has refereed for conferences and journals in these areas.

Affiliations and Expertise

Honorary Research Associate, Computer Science Department, University of Waikato, New Zealand

Christopher Pal

Affiliations and Expertise

Associate Professor, Département de génie informatique et génie logiciel, Polytechnique Montréal, and the Montréal Institute for Learning Algorithms at the Université de Montréal, Canada.