数据挖掘 实用机器学习工具与技术 英文版 原书第3版PDF电子书下载

PART Ⅰ INTRODUCTION TO DATA MINING 3

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

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

CHAPTER 4 Algorithms:The Basic Methods 85

4.1 Inferring Rudimentary Rules 86

Missing Values and Numeric Attributes 87

Discussion 89

4.2 Statistical Modeling 90

Missing Values and Numeric Attributes 94

Na？ve 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

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

4.7 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

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

5.7 Counting the Cost 163

Cost-Sensitive Classification 166

Cost-Sensitive Learning 167

Lift Charts 168

ROC Curves 172

Recall-precision Curves 174

Discussion 175

Cost Curves 177

5.8 Evaluating Numeric Prediction 180

5.9 Minimum Description Length Principle 183

5.10 Applying the MDL Principle to Clustering 186

5.11 Further Reading 187

PART Ⅱ ADVANCED DATA MINING 191

CHAPTER 6 Implementations:Real Machine Learning Schemes 191

6.1 Decision Trees 192

Numeric Attributes 193

Missing Values 194

Pruning 195

Estimating Error Rates 197

Complexity of Decision Tree Induction 199

From Trees to Rules 200

C4.5:Choices and Options 201

Cost-Complexity Pruning 202

Discussion 202

6.2 Classification Rules 203

Criteria for Choosing Tests 203

Missing Values,Numeric Attributes 204

Generating Good Rules 205

Using Global Optimization 208

Obtaining Rules from Partial Decision Trees 208

Rules with Exceptions 212

Discussion 215

6.3 Association Rules 216

Building a Frequent-Pattern Tree 216

Finding Large Item Sets 219

Discussion 222

6.4 Extending Linear Models 223

Maximum-Margin Hyperplane 224

Nonlinear Class Boundaries 226

Support Vector Regression 227

Kernel Ridge Regression 229

Kernel Perceptron 231

Multilayer Perceptrons 232

Radial Basis Function Networks 241

Stochastic Gradient Descent 242

Discussion 243

6.5 Instance-Based Learning 244

Reducing the Number of Exemplars 245

Pruning Noisy Exemplars 245

Weighting Attributes 246

Generalizing Exemplars 247

Distance Functions for Generalized Exemplars 248

Generalized Distance Functions 249

Discussion 250

6.6 Numeric Prediction with Local Linear Models 251

Model Trees 252

Building the Tree 253

Pruning the Tree 253

Nominal Attributes 254

Missing Values 254

Pseudocode for Model Tree Induction 255

Rules from Model Trees 259

Locally Weighted Linear Regression 259

Discussion 261

6.7 Bayesian Networks 261

Making Predictions 262

Learning Bayesian Networks 266

Specific Algorithms 268

Data Structures for Fast Learning 270

Discussion 273

6.8 Clustering 273

Choosing the Number of Clusters 274

Hierarchical Clustering 274

Example of Hierarchical Clustering 276

Incremental Clustering 279

Category Utility 284

Probability-Based Clustering 285

The EM Algorithm 287

Extending the Mixture Model 289

Bayesian Clustering 290

Discussion 292

6.9 Semisupervised Learning 294

Clustering for Classification 294

Co-training 296

EM and Co-training 297

Discussion 297

6.10 Multi-Instance Learning 298

Converting to Single-Instance Learning 298

Upgrading Learning Algorithms 300

Dedicated Multi-Instance Methods 301

Discussion 302

6.11 Weka Implementations 303

CHAPTER 7 Data Transformations 305

7.1 Attribute Selection 307

Scheme-Independent Selection 308

Searching the Attribute Space 311

Scheme-Specific Selection 312

7.2 Discretizing Numeric Attributes 314

Unsupervised Discretization 316

Entropy-Based Discretization 316

Other Discretization Methods 320

Entropy-Based versus Error-Based Discretization 320

Converting Discrete Attributes to Numeric Attributes 322

7.3 Projections 322

Principal Components Analysis 324

Random Projections 326

Partial Least-Squares Regression 326

Text to Attribute Vectors 328

Time Series 330

7.4 Sampling 330

Reservoir Sampling 330

7.5 Cleansing 331

Improving Decision Trees 332

Robust Regression 333

Detecting Anomalies 334

One-Class Learning 335

7.6 Transforming Multiple Classes to Binary Ones 338

Simple Methods 338

Error-Correcting Output Codes 339

Ensembles of Nested Dichotomies 341

7.7 Calibrating Class Probabilities 343

7.8 Further Reading 346

7.9 Weka Implementations 348

CHAPTER 8 Ensemble Learning 351

8.1 Combining Multiple Models 351

8.2 Bagging 352

Bias-Variance Decomposition 353

Bagging with Costs 355

8.3 Randomization 356

Randomization versus Bagging 357

Rotation Forests 357

8.4 Boosting 358

AdaBoost 358

The Power of Boosting 361

8.5 Additive Regression 362

Numeric Prediction 362

Additive Logistic Regression 364

8.6 Interpretable Ensembles 365

Option Trees 365

Logistic Model Trees 368

8.7 Stacking 369

8.8 Further Reading 371

8.9 Weka Implementations 372

Chapter 9 Moving on:Applications and Beyond 375

9.1 Applying Data Mining 375

9.2 Learning from Massive Datasets 378

9.3 Data Stream Learning 380

9.4 Incorporating Domain Knowledge 384

9.5 Text Mining 386

9.6 Web Mining 389

9.7 Adversarial Situmions 393

9.8 Ubiquitous Data Mining 395

9.9 Further Reading 397

PART Ⅲ THE WEKA DATA MINING WORKBENCH 403

CHAPTER 10 Introduction to Weka 403

10.1 What's in Weka? 403

10.2 How Do You Use It? 404

10.3 What Else Can You Do? 405

10.4 How Do You Get It? 406

CHAPTER 11 The Explorer 407

11.1 Getting Started 407

Preparing the Data 407

Loading the Data into the Explorer 408

Building a Decision Tree 410

Examining the Output 411

Doing It Again 413

Working with Models 414

When Things Go Wrong 415

11.2 Exploring the Explorer 416

Loading and Filtering Files 416

Training and Testing Learning Schemes 422

Do It Yourself:The User Classifier 424

Using a Metalearner 427

Clustering and Association Rules 429

Attribute Selection 430

Visualization 430

11.3 Filtering Algorithms 432

Unsupervised Attribute Filters 432

Unsupervised Instance Filters 441

Supervised Filters 443

11.4 Learning Algorithms 445

Bayesian Classifiers 451

Trees 454

Rules 457

Functions 459

Neural Networks 469

Lazy Classifiers 472

Multi-Instance Classifiers 472

Miscellaneous Classifiers 474

11.5 Metalearning Algorithms 474

Bagging and Randomization 474

Boosting 476

Combining Classifiers 477

Cost-Sensitive Learning 477

Optimizing Performance 478

Retargeting Classifiers for Different Tasks 479

11.6 Clustering Algorithms 480

11.7 Association-Rule Learners 485

11.8 Attribute Selection 487

Attribute Subset Evaluators 488

Single-Attribute Evaluators 490

Search Methods 492

CHAPTER 12 The Knowledge Flow Interface 495

12.1 Getting Started 495

12.2 Components 498

12.3 Configuring and Connecting the Components 500

12.4 Incremental Learning 502

CHAPTER 13 The Experimenter 505

13.11 Getting Started 505

Running an Experiment 506

Analyzing the Results 509

13.2 Simple Setup 510

13.3 Advanced Setup 511

13.4 The Analyze Panel 512

13.5 Distributing Processing over Several Machines 515

CHAPTER 14 The Command-Line Interface 519

14.1 Getting Started 519

14.2 The Structure of Weka 519

Classes,Instances,and Packages 520

The weka.core Package 520

The weka.classifiers Package 523

Other Packages 525

Javadoc Indexes 525

14.3 Command-Line Options 526

Genefic Options 526

Scheme-Specific Options 529

CHAPTER 15 Embedded Machine Learning 531

15.1 A Simple Data Mining Application 531

MessageClassifier() 536

uppdateData() 536

classifyMessage() 537

CHAPTER 16 Writing New Learning Schemes 539

16.1 An Example Classifier 539

buildClassifier() 540

makeTree() 540

computeInfoGain() 549

classifyInstance() 549

toSource() 550

main() 553

16.2 Conventions for Implementing Classifiers 555

Capabilities 555

CHAPTER 17 Tutorial Exercises for the Weka Explorer 559

17.1 Introduction to the Explorer Interface 559

Loading a Dataset 559

The Dataset Editor 560

Applying a Filter 561

The Visualize Panel 562

The Classify Panel 562

17.2 Nearest-Neighbor Learning and Decision Trees 566

The Glass Dataset 566

Attribute Selection 567

Class Noise and Nearest-Neighbor Learning 568

Varying the Amount of Training Data 569

Interactive Decision Tree Construction 569

17.3 Classification Boundaries 571

Visualizing 1R 571

Visualizing Nearest-Neighbor Learning 572

Visualizing Na？ve Bayes 573

Visualizing Decision Trees and Rule Sets 573

Messing with the Data 574

17.4 Preprocessing and Parameter Tuning 574

Discretization 574

More on Discretization 575

Automatic Attribute Selection 575

More on Automatic Attribute Selection 576

Automatic Parameter Tuning 577

17.5 Document Classification 578

Data with String Attributes 579

Classifying Actual Documents 580

Exploring the StringToWordVector Filter 581

17.6 Mining Association Rules 582

Association-Rule Mining 582

Mining a Real-Worid Dataset 584

Market Basket Analysis 584

REFERENCES 587

INDEX 607