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Ian H. Witten - Data Mining: Practical Machine Learning Tools and Techniques

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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 todays 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.
  • 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

Ian H. Witten: author's other books


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Table of Contents List of tables Tables in Chapter 1 Tables in Chapter 2 - photo 1
Table of Contents
List of tables
  1. Tables in Chapter 1
  2. Tables in Chapter 2
  3. Tables in Chapter 3
  4. Tables in Chapter 4
  5. Tables in Chapter 5
  6. Tables in Chapter 6
  7. Tables in Chapter 7
  8. Tables in Chapter 8
  9. Tables in Chapter 9
  10. Tables in Chapter 10
  11. Tables in Chapter 13
  12. Tables in Appendix A
List of illustrations
  1. Figures in Chapter 1
  2. Figures in Chapter 2
  3. Figures in Chapter 3
  4. Figures in Chapter 4
  5. Figures in Chapter 5
  6. Figures in Chapter 6
  7. Figures in Chapter 7
  8. Figures in Chapter 8
  9. Figures in Chapter 9
  10. Figures in Chapter 10
  11. Figures in Chapter 12
  12. Figures in Chapter 13
  13. Figures in Appendix A
Landmarks
Table of Contents
Data Mining
Practical Machine Learning Tools and Techniques

Fourth Edition

Ian H. Witten

University of Waikato, Hamilton, New Zealand

Eibe Frank

University of Waikato, Hamilton, New Zealand

Mark A. Hall

University of Waikato, Hamilton, New Zealand

Christopher J. Pal

Polytechnique Montral, Montreal, Quebec, Canada

Copyright Morgan Kaufmann is an imprint of Elsevier 50 Hampshire Street 5th - photo 2

Copyright

Morgan Kaufmann is an imprint of Elsevier

50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States

Copyright 2017, 2011, 2005, 2000 Elsevier Inc. All rights reserved.

No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publishers permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions.

This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein).

Notices

Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary.

Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility.

To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein.

British Library Cataloguing-in-Publication Data

A catalogue record for this book is available from the British Library

Library of Congress Cataloging-in-Publication Data

A catalog record for this book is available from the Library of Congress

ISBN: 978-0-12-804291-5

For Information on all Morgan Kaufmann publications visit our website at https://www.elsevier.com

Publisher Todd Green Acquisition Editor Tim Pitts Editorial Project - photo 3

Publisher: Todd Green

Acquisition Editor: Tim Pitts

Editorial Project Manager: Charlotte Kent

Production Project Manager: Nicky Carter

Designer: Matthew Limbert

Typeset by MPS Limited, Chennai, India

List of Figures
Rules for the contact lens data.13
Decision tree for the contact lens data.14
Decision trees for the labor negotiations data.18
Life cycle of a data mining project.29
A family tree and two ways of expressing the sister-of relation.48
ARFF file for the weather data.58
Multi-instance ARFF file for the weather data.60
A linear regression function for the CPU performance data.69
A linear decision boundary separating Iris setosas from Iris versicolors.70
Constructing a decision tree interactively: (A) creating a rectangular test involving petallength and petalwidth; (B) the resulting (unfinished) decision tree.73
Models for the CPU performance data: (A) linear regression; (B) regression tree; (C) model tree.74
Decision tree for a simple disjunction.76
The exclusive-or problem.77
Decision tree with a replicated subtree.77
Rules for the iris data.81
The shapes problem.82
Different ways of partitioning the instance space.86
Different ways of representing clusters.88
Pseudocode for 1R.93
Tree stumps for the weather data.106
Expanded tree stumps for the weather data.108
Decision tree for the weather data.109
Tree stump for the ID code attribute.111
Covering algorithm: (A) covering the instances; (B) decision tree for the same problem.113
The instance space during operation of a covering algorithm.115
Pseudocode for a basic rule learner.118
(A) Finding all item sets with sufficient coverage; (B) finding all sufficiently accurate association rules for a k-item set.127
Logistic regression: (A) the logit transform; (B) example logistic regression function.130
The perceptron: (A) learning rule; (B) representation as a neural network.132
The Winnow algorithm: (A) unbalanced version; (B) balanced version.134
A kD-tree for four training instances: (A) the tree; (B) instances and splits.137
Using a kD-tree to find the nearest neighbor of the star.137
Ball tree for 16 training instances: (A) instances and balls; (B) the tree.139
Ruling out an entire ball (gray) based on a target point (star) and its current nearest neighbor.140
Iterative distance-based clustering.143
A ball tree: (A) two cluster centers and their dividing line; (B) corresponding tree.145
Hierarchical clustering displays.149
Clustering the weather data.151
Hierarchical clusterings of the iris data.153
A hypothetical lift chart.185
Analyzing the expected benefit of a mailing campaign when the cost of mailing is (A) $0.50 and (B) $0.80.
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