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 -- Second Edition -- Third Edition -- Acknowledgments -- About the Authors -- Part I Introduction to Data Mining -- CHAPTER 1 What's It All About? -- 1.1 Data mining and machine learning -- Describing Structural Patterns -- Machine Learning -- Data Mining -- 1.2 Simple examples: the weather and other problems -- The Weather Problem -- Contact Lenses: An Idealized Problem -- Irises: A Classic Numeric Dataset -- CPU Performance: Introducing Numeric Prediction -- Labor Negotiations: A More Realistic Example -- Soybean Classification: A Classic Machine Learning Success -- 1.3 Fielded applications -- Web Mining -- Decisions Involving Judgment -- Screening Images -- Load Forecasting -- Diagnosis -- Marketing and Sales -- Other Applications -- 1.4 Machine learning and statistics -- 1.5 Generalization as search -- 1.6 Data mining and ethics -- Reidentification -- Using Personal Information -- Wider Issues -- 1.7 Further reading -- CHAPTER 2 Input: -- 2.1 What's a concept? -- 2.2 What's in an example? -- Relations -- Other Example Types -- 2.3 What's in an attribute? -- 2.4 Preparing the input -- Gathering the Data Together -- ARFF Format -- Sparse Data -- Attribute Types -- Missing Values -- Inaccurate Values -- Getting to Know Your Data -- 2.5 Further reading -- CHAPTER 3 Output: -- 3.1 Tables -- 3.2 Linear models -- 3.3 Trees -- 3.4 Rules -- Classification Rules -- Association Rules -- Rules with Exceptions -- More Expressive Rules -- 3.5 Instance-based representation -- 3.6 Clusters -- 3.7 Further reading -- CHAPTER 4 Algorithms: -- 4.1 InFerring rudimentary rules -- Missing Values and Numeric Attributes -- Discussion -- 4.2 Statistical modeling.

Missing Values and Numeric Attributes -- Naïve Bayes for Document Classification -- Discussion -- 4.3 Divide-and-conquer: constructing decision trees -- Calculating Information -- Highly Branching Attributes -- Discussion -- 4.4 Covering algorithms: constructing rules -- Rules versus Trees -- A Simple Covering Algorithm -- Rules versus Decision Lists -- 4.5 Mining association rules -- Item Sets -- Association Rules -- Generating Rules Efficiently -- Discussion -- 4.6 Linear models -- Numeric Prediction: Linear Regression -- Linear Classification: Logistic Regression -- Linear Classification Using the Perceptron -- Linear Classification Using Winnow -- 4.7 Instance-based learning -- Distance Function -- Finding Nearest Neighbors Efficiently -- Discussion -- 4.8 Clustering -- Iterative Distance-Based Clustering -- Faster Distance Calculations -- Discussion -- 4.9 Multi-instance learning -- Aggregating the Input -- Aggregating the Output -- Discussion -- 4.10 Further reading -- 4.11 Weka implementations -- CHAPTER 5 Credibility: -- 5.1 Training and testing -- 5.2 Predicting performance -- 5.3 Cross-validation -- 5.4 Other estimates -- Leave-One-Out Cross-Validation -- The Bootstrap -- 5.5 Comparing data mining schemes -- 5.6 Predicting probabilities -- Quadratic Loss Function -- Informational Loss Function -- Discussion -- 5.7 Counting the cost -- Cost-Sensitive Classification -- Cost-Sensitive Learning -- Lift Charts -- ROC Curves -- Recall-Precision Curves -- Discussion -- Cost Curves -- 5.8 Evaluating numeric prediction -- 5.9 Minimum description length principle -- 5.10 Applying the MDL principle to clustering -- 5.11 Further reading -- Part II Advanced Data Mining -- CHAPTER 6 Implementations: -- 6.1 Decision trees -- Numeric Attributes -- Missing Values -- Pruning -- Estimating Error Rates -- Complexity of Decision Tree Induction.

From Trees to Rules -- C4.5: Choices and Options -- Cost-Complexity Pruning -- Discussion -- 6.2 Classification rules -- Criteria for Choosing Tests -- Missing Values, Numeric Attributes -- Generating Good Rules -- Using Global Optimization -- Obtaining Rules from Partial Decision Trees -- Rules with Exceptions -- Discussion -- 6.3 Association rules -- Building a Frequent-Pattern Tree -- Finding Large Item Sets -- Discussion -- 6.4 Extending linear models -- Maximum-Margin Hyperplane -- Nonlinear Class Boundaries -- Support Vector Regression -- Kernel Ridge Regression -- Kernel Perceptron -- Multilayer Perceptrons -- Backpropagation -- Radial Basis Function Networks -- Stochastic Gradient Descent -- Discussion -- 6.5 Instance-based learning -- Reducing the Number of Exemplars -- Pruning Noisy Exemplars -- Weighting Attributes -- Generalizing Exemplars -- Distance Functions for Generalized Exemplars -- Generalized Distance Functions -- Discussion -- 6.6 Numeric prediction with local linear models -- Model Trees -- Building the Tree -- Pruning the Tree -- Nominal Attributes -- Missing Values -- Pseudocode for Model Tree Induction -- Rules from Model Trees -- Locally Weighted Linear Regression -- Discussion -- 6.7 Bayesian networks -- Making Predictions -- Learning Bayesian Networks -- Specific Algorithms -- Data Structures for Fast Learning -- Discussion -- 6.8 Clustering -- Choosing the Number of Clusters -- Hierarchical Clustering -- Example of Hierarchical Clustering -- Incremental Clustering -- Probability-Based Clustering -- The EM Algorithm -- Extending the Mixture Model -- Bayesian Clustering -- Discussion -- 6.9 Semisupervised learning -- Clustering for Classification -- Co-training -- EM and Co-training -- Discussion -- 6.10 Multi-instance learning -- Converting to Single-Instance Learning -- Upgrading Learning Algorithms.

Dedicated Multi-Instance Methods -- Discussion -- 6.11 Weka implementations -- CHAPTER 7 Data Transformations -- 7.1 Attribute selection -- Scheme-Independent Selection -- Searching the Attribute Space -- Scheme-Specific Selection -- 7.2 Discretizing numeric attributes -- Unsupervised Discretization -- Entropy-Based Discretization -- Other Discretization Methods -- Entropy-Based versus Error-Based Discretization -- Converting Discrete Attributes to Numeric Attributes -- 7.3 Projections -- Principal Components Analysis -- Random Projections -- Partial Least-Squares Regression -- Text to Attribute Vectors -- Time Series -- 7.4 Sampling -- Reservoir Sampling -- 7.5 Cleansing -- Improving Decision Trees -- Robust Regression -- Detecting Anomalies -- One-Class Learning -- Outlier Detection -- Generating Artificial Data -- 7.6 Transforming multiple classes to binary ones -- Simple Methods -- Error-Correcting Output Codes -- Ensembles of Nested Dichotomies -- 7.7 Calibrating class probabilities -- 7.8 Further reading -- 7.9 Weka implementations -- CHAPTER 8 Ensemble Learning -- 8.1 Combining multiple models -- 8.2 Bagging -- Bias-Variance Decomposition -- Bagging with Costs -- 8.3 Randomization -- Randomization versus Bagging -- Rotation Forests -- 8.4 Boosting -- AdaBoost -- The Power of Boosting -- 8.5 Additive regression -- Numeric Prediction -- Additive Logistic Regression -- 8.6 Interpretable ensembles -- Option Trees -- Logistic Model Trees -- 8.7 Stacking -- 8.8 Further reading -- 8.9 Weka implementations -- CHAPTER 9 Moving on: -- 9.1 Applying data mining -- 9.2 Learning from massive datasets -- 9.3 Data stream learning -- 9.4 Incorporating domain knowledge -- 9.5 Text mining -- 9.6 Web mining -- 9.7 Adversarial situations -- 9.8 Ubiquitous data mining -- 9.9 Further reading -- Part III The Weka Data Mining Workbench.

CHAPTER 10 Introduction to Weka -- 10.1 What's in weka? -- 10.2 How do you use it? -- 10.3 What else can you do? -- 10.4 How do you get it? -- CHAPTER 11 The Explorer -- 11.1 Getting started -- Preparing the Data -- Loading the Data into the Explorer -- Building a Decision Tree -- Examining the Output -- Doing It Again -- Working with Models -- When Things Go Wrong -- 11.2 Exploring the explorer -- Loading and Filtering Files -- Converting Files to ARFF -- Using Filters -- Training and Testing Learning Schemes -- Do It Yourself: The User Classifier -- Using a Metalearner -- Clustering and Association Rules -- Attribute Selection -- Visualization -- 11.3 Filtering algorithms -- Unsupervised Attribute Filters -- Adding and Removing Attributes -- Changing Values -- Conversions -- String Conversion -- Multi-Instance Data -- Time Series -- Randomizing -- Unsupervised Instance Filters -- Randomizing and Subsampling -- Sparse Instances -- Supervised Filters -- Supervised Attribute Filters -- Supervised Instance Filters -- 11.4 Learning algorithms -- Bayesian Classifiers -- Trees -- Rules -- Functions -- Neural Networks -- Lazy Classifiers -- Multi-Instance Classifiers -- Miscellaneous Classifiers -- 11.5 Metalearning algorithms -- Bagging and Randomization -- Boosting -- Combining Classifiers -- Cost-Sensitive Learning -- Optimizing Performance -- Retargeting Classifiers for Different Tasks -- 11.6 Clustering algorithms -- 11.7 Association-rule learners -- 11.8 Attribute selection -- Attribute Subset Evaluators -- Single-Attribute Evaluators -- Search methods -- CHAPTER 12 The Knowledge Flow Interface -- 12.1 Getting started -- 12.2 Components -- 12.3 Configuring and connecting the components -- 12.4 Incremental learning -- CHAPTER 13 The Experimenter -- 13.1 Getting started -- Running an Experiment -- Analyzing the Results -- 13.2 Simple setup.

13.3 Advanced setup.