Data mining
Free

Data mining

By I. H. Witten
Free
Book Description

practical machine learning tools and techniques

Table of Contents
  • 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
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