Data Mining: Practical Machine Learning Tools and Techniques
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Table of contents
- Cover
- Forewordv
- Table of contentsvii
- List of Figuresxvii
- List of Tablesxxi
- Prefacexxiii
- Updated and revised contentxxvii
- Acknowledgmentsxxix
- Part I Machine Learning Tools and Techniques1
- 1 What's It All About?3
- 1.1 Data mining and machine learning4
- 1.2 Simple examples: The weather problem and others9
- 1.3 Fielded applications22
- 1.4 Machine learning and statistics29
- 1.5 Generalization as search30
- 1.6 Data mining and ethics35
- 1.7 Further reading37
- 2 Input: Concepts, Instances, and Attributes41
- 2.1 What's a concept?42
- 2.2 What's in an example?45
- 2.3 What's in an attribute?49
- 2.4 Preparing the input52
- 2.5 Further reading60
- 3 Output: Knowledge Representation61
- 3.1 Decision tables62
- 3.2 Decision trees62
- 3.3 Classification rules65
- 3.4 Association rules69
- 3.5 Rules with exceptions70
- 3.6 Rules involving relations73
- 3.7 Trees for numeric prediction76
- 3.8 Instance-based representation76
- 3.9 Clusters81
- 3.10 Further reading82
- 4 Algorithms: The Basic Methods83
- 4.1 Inferring rudimentary rules84
- 4.2 Statistical modeling88
- 4.3 Divide-and-conquer: Constructing decision trees97
- 4.4 Covering algorithms: Constructing rules105
- 4.5 Mining association rules112
- 4.6 Linear models119
- 4.7 Instance-based learning128
- 4.8 Clustering136
- 4.9 Further reading139
- 5 Credibility: Evaluating What’s Been Learned143
- 5.1 Training and testing144
- 5.2 Predicting performance146
- 5.3 Cross-validation149
- 5.4 Other estimates151
- 5.5 Comparing data mining methods153
- 5.6 Predicting probabilities157
- 5.7 Counting the cost161
- 5.8 Evaluating numeric prediction176
- 5.9 The minimum description length principle179
- 5.10 Applying the MDL principle to clustering183
- 5.11 Further reading184
- 6 Implementations: Real Machine Learning Schemes187
- 6.1 Decision trees189
- 6.2 Classification rules200
- 6.3 Extending linear models214
- 6.4 Instance-based learning235
- 6.5 Numeric prediction243
- 6.6 Clustering254
- 6.7 Bayesian networks271
- 7 Transformations: Engineering the input and output285
- 7.1 Attribute selection288
- 7.2 Discretizing numeric attributes296
- 7.3 Some useful transformations305
- 7.4 Automatic data cleansing312
- 7.5 Combining multiple models315
- 7.6 Using unlabeled data337
- 7.7 Further reading341
- 8 Moving on: Extensions and Applications345
- 8.1 Learning from massive datasets346
- 8.2 Incorporating domain knowledge349
- 8.3 Text and Web mining351
- 8.4 Adversarial situations356
- 8.5 Ubiquitous data mining358
- 8.6 Further reading361
- Part II The Weka Machine Learning Workbench363
- 9 Introduction to Weka365
- 9.1 What's in Weka?366
- 9.2 How do you use it?367
- 9.3 What else can you do?368
- 9.4 How do you get it?368
- 10 The Explorer369
- 10.1 Getting started369
- 10.2 Exploring the Explorer380
- 10.3 Filtering algorithms393
- 10.4 Learning algorithms403
- 10.5 Metalearning algorithms414
- 10.6 Clustering algorithms418
- 10.7 Association-rule learners419
- 10.8 Attribute selection420
- 11 The Knowledge Flow Interface427
- 11.1 Getting started427
- 11.2 The Knowledge Flow components430
- 11.3 Configuring and connecting the components431
- 11.4 Incremental learning433
- 12 The Experimenter437
- 12.1 Getting started438
- 12.2 Simple setup441
- 12.3 Advanced setup442
- 12.4 The Analyze panel443
- 12.5 Distributing processing over several machines445
- 13 The Command-line Interface449
- 13.1 Getting started449
- 13.2 The structure of Weka450
- 13.3 Command-line options456
- 14 Embedded Machine Learning461
- 14.1 A simple data mining application461
- 14.2 Going through the code462
- 15 Writing New Learning Schemes471
- 15.1 An example classifier471
- 15.2 Conventions for implementing classifiers483
- References485
- Index505
- About the Authors525
As with any burgeoning technology that enjoys commercial attention, the use of data mining is surrounded by a great deal of hype. Exaggerated reports tell of secrets that can be uncovered by setting algorithms loose on oceans of data. But there is no magic in machine learning, no hidden power, no alchemy. Instead there is an identifiable body of practical techniques that can extract useful information from raw data. This book describes these techniques and shows how they work.
The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same, it has been updated to reflect the changes that have taken place over five years, and now has nearly double the references. The highlights for the new edition include thirty new technique sections; an enhanced Weka machine learning workbench, which now features an interactive interface; comprehensive information on neural networks; a new section on Bayesian networks; plus much more.
* Algorithmic methods at the heart of successful data mining—including tried and true techniques as well as leading edge methods
* Performance improvement techniques that work by transforming the input or output
* Downloadable Weka, a collection of machine learning algorithms for data mining tasks, including tools for data pre-processing, classification, regression, clustering, association rules, and visualization—in a new, interactive interface
The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same, it has been updated to reflect the changes that have taken place over five years, and now has nearly double the references. The highlights for the new edition include thirty new technique sections; an enhanced Weka machine learning workbench, which now features an interactive interface; comprehensive information on neural networks; a new section on Bayesian networks; plus much more.
* Algorithmic methods at the heart of successful data mining—including tried and true techniques as well as leading edge methods
* Performance improvement techniques that work by transforming the input or output
* Downloadable Weka, a collection of machine learning algorithms for data mining tasks, including tools for data pre-processing, classification, regression, clustering, association rules, and visualization—in a new, interactive interface