Data Mining: Practical Machine Learning Tools and Techniques

Front Cover
Morgan Kaufmann, Oct 1, 2016 - Computers - 654 pages

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 https://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.
  • 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
 

Selected pages

Contents

II More advanced machine learning schemes
205
Theoretical foundations
533
The WEKA workbench
553
References
573
Index
601
Back Cover
622
Copyright

Common terms and phrases

applied association rules attribute values autoencoder Bayesian network Chapter class value classifier clustering computing conditional random fields corresponding covariance cross-validation data mining dataset decision tree deep learning described discretization distribution document ensemble evaluation example factor graph FIGURE filter Gaussian given gradient descent hidden humidity implementation input instance-based learning item sets iteration kernel layer learner learning algorithm learning scheme linear models linear regression logistic regression machine learning Markov matrix missing values model trees multi-instance multiclass Naıve Bayes neural networks node nominal attributes normal numeric attributes optimization output overfitting package parameters perceptron performance play5yes principal component analysis probabilistic probability estimates problem produce pruning random variable result rule set sample Section selection simple split statistical stochastic gradient descent structure subset subtree support vector machines Table techniques test instance test set tion training data training instances training set transformed two-class weather data weights WEKA windy Yes Yes zero

About the author (2016)

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.

Christopher J. Pal is a Canada CIFAR AI Chair and a full professor at the Department of Computer Engineering and Software Engineering at Polytechnique Montréal. Pal’s research interests include computer vision and pattern recognition, computational photography, natural language processing, statistical machine learning and applications to human computer interaction.

Bibliographic information

TitleData Mining: Practical Machine Learning Tools and Techniques
The Morgan Kaufmann Series in Data Management Systems
AuthorsIan H. Witten, Eibe Frank, Mark A. Hall, Christopher J. Pal
Edition4, reprint
PublisherMorgan Kaufmann, 2016
ISBN0128043571, 9780128043578
Length654 pages
Subjects ›  › 

Computers / Artificial Intelligence / General
Computers / Data Science / General
Computers / Database Administration & Management
Computers / General
Computers / Management Information Systems
Language Arts & Disciplines / Library & Information Science / Archives & Special Libraries
  
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