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Comments "If you have data that you want to analyze and understand, this book and the associated Weka toolkit are an excellent way to start." -Jim Gray, Microsoft Research Features Explains how data mining algorithms work. Helps you select appropriate approaches to particular problems and to compare and evaluate the results of different techniques. Covers performance improvement techniques, including input preprocessing and combining output from different methods. Shows you how to use the Weka machine learning workbench. Table of Contents for the 2nd Edition: Sections and chapters with new material are marked in red. Preface Part I: Practical Machine Learning Tools and Techniques 1. What’s it all about? 1.1 Data mining and machine learning 1.2 Simple examples: the weather problem and others 1.3 Fielded applications 1.4 Machine learning and statistics 1.5 Generalization as search 1.6 Data mining and ethics 1.7 Further reading 2. Input: Concepts, instances, attributes 2.1 What’s a concept? 2.2 What’s in an example? 2.3 What’s in an attribute? 2.4 Preparing the input 2.5 Further reading 3. Output: Knowledge representation 3.1 Decision tables 3.2 Decision trees 3.3 Classification rules 3.4 Association rules 3.5 Rules with exceptions 3.6 Rules involving relations 3.7 Trees for numeric prediction 3.8 Instance-based representation 3.9 Clusters 3.10 Further reading 4. Algorithms: The basic methods 4.1 Inferring rudimentary rules 4.2 Statistical modeling 4.3 Divide-and-conquer: constructing decision trees 4.4 Covering algorithms: constructing rules 4.5 Mining association rules 4.6 Linear models 4.7 Instance-based learning 4.8 Clustering 4.9 Further reading 5. Credibility: Evaluating what’s been learned 5.1 Training and testing 5.2 Predicting performance 5.3 Cross-validation 5.4 Other estimates 5.5 Comparing data mining schemes 5.6 Predicting probabilities 5.7 Counting the cost 5.8 Evaluating numeric prediction 5.9 The minimum description length principle 5.10 Applying MDL to clustering 5.11 Further reading 6. Implementations: Real machine learning schemes 6.1 Decision trees 6.2 Classification rules 6.3 Extending linear models 6.4 Instance-based learning 6.5 Numeric prediction 6.6 Clustering 6.7 Bayesian networks 7. Transformations: Engineering the input and output 7.1 Attribute selection 7.2 Discretizing numeric attributes 7.3 Some useful transformations 7.4 Automatic data cleansing 7.5 Combining multiple models 7.6 Using unlabeled data 7.7 Further reading 8. Moving on: Extensions and applications 8.1 Learning from massive datasets 8.2 Incorporating domain knowledge 8.3 Text and Web mining 8.4 Adversarial situations 8.5 Ubiquitous data mining 8.6 Further reading Part II: The Weka machine learning workbench 9. Introduction to Weka 9.1 What’s in Weka? 9.2 How do you use it? 9.3 What else can you do? 10. The Explorer 10.1 Getting started 10.2 Exploring the Explorer 10.3 Filtering algorithms 10.4 Learning algorithms 10.5 Meta-learning algorithms 10.6 Clustering algorithms 10.7 Association-rule learners 10.8 Attribute selection 11. The Knowledge Flow interface 11.1 Getting started 11.2 Knowledge Flow components 11.3 Configuring and connecting the components 11.4 Incremental learning 12. The Experimenter 12.1 Getting started 12.2 Simple setup 12.3 Advanced setup 12.4 The Analyze panel 12.5 Distributing processing over several machines 13. The command-line interface 13.1 Getting started 13.2 The structure of Weka 13.3 Command-line options 14. Embedded machine learning 15. Writing new learning schemes References Index 附件(3.8MB) Learning_Tools_And_Techniques,Second_Edition_-_Fly.part1.rar

[ebook]Data Mining: Practical Machine Learning Tools and Te