第1页 / 共665页
第2页 / 共665页
第3页 / 共665页
第4页 / 共665页
第5页 / 共665页
第6页 / 共665页
第7页 / 共665页
第8页 / 共665页
Weka同步课本-Data_Mining.pdf
Front cover
Data Mining: Practical Machine Learning Tools and Techniques
Copyright page
Table of contents
List of Figures
List of Tables
Preface
Updated and revised content
Acknowledgments
About the Authors
PART I: Introduction to Data Mining
Chapter 1: What’s It All About?
Data mining and machine learning
Simple examples: the weather and other problems
Fielded applications
Machine learning and statistics
Generalization as search
Data mining and ethics
Further reading
Chapter 2: Input: Concepts, Instances, and Attributes
What’s a concept?
What’s in an example?
What’s in an attribute?
Preparing the input
Further reading
Chapter 3: Output: Knowledge Representation
Tables
Linear models
Trees
Rules
Instance-based representation
Clusters
Further reading
Chapter 4: Algorithms: The Basic Methods
InFerring rudimentary rules
Statistical modeling
Divide-and-conquer: constructing decision trees
Covering algorithms: constructing rules
Mining association rules
Linear models
Instance-based learning
Clustering
Multi-instance learning
Further reading
Weka implementations
Chapter 5: Credibility: Evaluating What’s Been Learned
Training and testing
Predicting performance
Cross-validation
Other estimates
Comparing data mining schemes
Predicting probabilities
Counting the cost
Evaluating numeric prediction
Minimum description length principle
Applying the MDL principle to clustering
Further reading
Part 2: Advanced Data Mining
Chapter 6: Implementations: Real Machine Learning Schemes
Decision trees
Classification rules
Association rules
Extending linear models
Instance-based learning
Numeric prediction with local linear models
Bayesian networks
Clustering
Semisupervised learning
Multi-instance learning
Weka implementations
Chapter 7: Data Transformations
Attribute selection
Discretizing numeric attributes
Projections
Sampling
Cleansing
Transforming multiple classes to binary ones
Calibrating class probabilities
Further reading
Weka implementations
Chapter 8: Ensemble Learning
Combining multiple models
Bagging
Randomization
Boosting
Additive regression
Interpretable ensembles
Stacking
Further reading
Weka implementations
Chapter 9: Moving on: Applications and Beyond
Applying data mining
Learning from massive datasets
Data stream learning
Incorporating domain knowledge
Text mining
Web mining
Adversarial situations
Ubiquitous data mining
Further reading
PART III: The Weka Data Mining Workbench
Chapter 10: Introduction to Weka
What’s in weka?
How do you use it?
What else can you do?
How do you get it?
Chapter 11: The Explorer
Getting started
Exploring the explorer
Filtering algorithms
Learning algorithms
Metalearning algorithms
Clustering algorithms
Association-rule learners
Attribute selection
Chapter 12: The Knowledge Flow Interface
Getting started
Components
Configuring and connecting the components
Incremental learning
Chapter 13: The Experimenter
Getting started
Simple setup
Advanced setup
The analyze panel
Distributing processing over several machines
Chapter 14: The Command-Line Interface
Getting started
The structure of weka
Command-line options
Chapter 15: Embedded Machine Learning
A simple data mining application
Chapter 16: Writing New Learning Schemes
An example classifier
Conventions for implementing classifiers
Chapter 17: Tutorial Exercises for the Weka Explorer
Introduction to the explorer interface
Nearest-neighbor learning and decision trees
Classification boundaries
Preprocessing and parameter tuning
Document classification
Mining association rules
References
Index
Data Mining
Third Edition
This page intentionally left blank
Data Mining
Practical Machine Learning
Tools and Techniques
Third Edition
Ian H. Witten
Eibe Frank
Mark A. Hall
AMSTERDAM • BOSTON • HEIDELBERG • LONDON
NEW YORK • OXFORD • PARIS • SAN DIEGO
SAN FRANCISCO • SINGAPORE • SYDNEY • TOKYO
Morgan Kaufmann Publishers is an imprint of Elsevier
Morgan Kaufmann Publishers is an imprint of Elsevier
30 Corporate Drive, Suite 400, Burlington, MA 01803, USA
This book is printed on acid-free paper.
Copyright © 2011 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 Publisher’s 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.
Library of Congress Cataloging-in-Publication Data
Witten, I. H. (Ian H.)
Data mining : practical machine learning tools and techniques.—3rd ed. /
Ian H. Witten, Frank Eibe, Mark A. Hall.
1. Data mining.
QA76.9.D343W58 2011
006.3′12—dc22
p. cm.—(The Morgan Kaufmann series in data management systems)
ISBN 978-0-12-374856-0 (pbk.)
I. Hall, Mark A.
II. Title.
2010039827
British Library Cataloguing-in-Publication Data
A catalogue record for this book is available from the British Library.
For information on all Morgan Kaufmann publications, visit our
website at www.mkp.com or www.elsevierdirect.com
Printed in the United States
11 12 13 14 15 10 9 8 7 6 5 4 3 2 1
Working together to grow
libraries in developing countries
www.elsevier.com | www.bookaid.org | www.sabre.org
Contents
LIST OF FIGURES .................................................................................................xv
LIST OF TABLES ..................................................................................................xix
PREFACE ...............................................................................................................xxi
Updated and Revised Content ...........................................................................xxv
Second Edition ...............................................................................................xxv
Third Edition .................................................................................................xxvi
ACKNOWLEDGMENTS ....................................................................................xxix
ABOUT THE AUTHORS ..................................................................................xxxiii
INTRODUCTION TO DATA MINING
PART I
CHAPTER 1 What’s It All About? ................................................................ 3
1.1 Data Mining and Machine Learning ..............................................3
Describing Structural Patterns ........................................................5
Machine Learning ...........................................................................7
Data Mining ....................................................................................8
1.2 Simple Examples: The Weather Problem and Others ....................9
The Weather Problem .....................................................................9
Contact Lenses: An Idealized Problem ........................................12
Irises: A Classic Numeric Dataset ................................................13
CPU Performance: Introducing Numeric Prediction....................15
Labor Negotiations: A More Realistic Example ..........................15
Soybean Classification: A Classic Machine Learning Success ....19
1.3 Fielded Applications .....................................................................21
Web Mining...................................................................................21
Decisions Involving Judgment .....................................................22
Screening Images ..........................................................................23
Load Forecasting ...........................................................................24
Diagnosis .......................................................................................25
Marketing and Sales .....................................................................26
Other Applications ........................................................................27
1.4 Machine Learning and Statistics ..................................................28
1.5 Generalization as Search .............................................................29
1.6 Data Mining and Ethics ................................................................33
Reidentification .............................................................................33
Using Personal Information ..........................................................34
Wider Issues ..................................................................................35
1.7 Further Reading ............................................................................36
v
vi
Contents
CHAPTER 2
Input: Concepts, Instances, and Attributes ............................. 39
2.1 What’s a Concept? ........................................................................40
2.2 What’s in an Example? .................................................................42
Relations ........................................................................................43
Other Example Types ....................................................................46
2.3 What’s in an Attribute? .................................................................49
2.4 Preparing the Input .......................................................................51
Gathering the Data Together .........................................................51
ARFF Format ................................................................................52
Sparse Data ...................................................................................56
Attribute Types ..............................................................................56
Missing Values ..............................................................................58
Inaccurate Values ..........................................................................59
Getting to Know Your Data ..........................................................60
2.5 Further Reading ............................................................................60
CHAPTER 3 Output: Knowledge Representation ........................................ 61
3.1 Tables ............................................................................................61
3.2 Linear Models ...............................................................................62
3.3 Trees ..............................................................................................64
3.4 Rules ..............................................................................................67
Classification Rules .......................................................................69
Association Rules ..........................................................................72
Rules with Exceptions ..................................................................73
More Expressive Rules .................................................................75
3.5
Instance-Based Representation .....................................................78
3.6 Clusters ..........................................................................................81
3.7 Further Reading ............................................................................83
4.1
CHAPTER 4 Algorithms: The Basic Methods ............................................. 85
Inferring Rudimentary Rules ........................................................86
Missing Values and Numeric Attributes .......................................87
Discussion .....................................................................................89
4.2 Statistical Modeling ......................................................................90
Missing Values and Numeric Attributes ......................................94
Naïve Bayes for Document Classification....................................97
Discussion .....................................................................................99
4.3 Divide-and-Conquer: Constructing Decision Trees .....................99
Calculating Information ..............................................................103
Highly Branching Attributes .......................................................105
Discussion ...................................................................................107
Contents
vii
4.4 Covering Algorithms: Constructing Rules .................................108
Rules versus Trees ......................................................................109
A Simple Covering Algorithm .................................................... 110
Rules versus Decision Lists ........................................................ 115
4.5 Mining Association Rules ........................................................... 116
Item Sets ...................................................................................... 116
Association Rules ........................................................................ 119
Generating Rules Efficiently .......................................................122
Discussion ...................................................................................123
4.6 Linear Models .............................................................................124
Numeric Prediction: Linear Regression .....................................124
Linear Classification: Logistic Regression .................................125
Linear Classification Using the Perceptron ................................127
Linear Classification Using Winnow ..........................................129
Instance-Based Learning .............................................................131
Distance Function .......................................................................131
Finding Nearest Neighbors Efficiently .......................................132
Discussion ...................................................................................137
4.8 Clustering ....................................................................................138
Iterative Distance-Based Clustering ...........................................139
Faster Distance Calculations .......................................................139
Discussion ...................................................................................141
4.9 Multi-Instance Learning ..............................................................141
Aggregating the Input .................................................................142
Aggregating the Output ..............................................................142
Discussion ...................................................................................142
4.10 Further Reading ..........................................................................143
4.11 Weka Implementations ................................................................145
4.7
CHAPTER 5 Credibility: Evaluating What’s Been Learned ........................ 147
5.1 Training and Testing ...................................................................148
5.2 Predicting Performance ...............................................................150
5.3 Cross-Validation ..........................................................................152
5.4 Other Estimates ...........................................................................154
Leave-One-Out Cross-Validation ................................................154
The Bootstrap ..............................................................................155
5.5 Comparing Data Mining Schemes ..............................................156
5.6 Predicting Probabilities ...............................................................159
Quadratic Loss Function .............................................................160
Informational Loss Function .......................................................161
Discussion ...................................................................................162
相关推荐
2023年江西萍乡中考道德与法治真题及答案.doc
2012年重庆南川中考生物真题及答案.doc
2013年江西师范大学地理学综合及文艺理论基础考研真题.doc
2020年四川甘孜小升初语文真题及答案I卷.doc
2020年注册岩土工程师专业基础考试真题及答案.doc
2023-2024学年福建省厦门市九年级上学期数学月考试题及答案.doc
2021-2022学年辽宁省沈阳市大东区九年级上学期语文期末试题及答案.doc
2022-2023学年北京东城区初三第一学期物理期末试卷及答案.doc
2018上半年江西教师资格初中地理学科知识与教学能力真题及答案.doc
2012年河北国家公务员申论考试真题及答案-省级.doc
2020-2021学年江苏省扬州市江都区邵樊片九年级上学期数学第一次质量检测试题及答案.doc
2022下半年黑龙江教师资格证中学综合素质真题及答案.doc
