- Cover
- Series
- Title Page
- Copyright
- Dedication
- Preface
- What is Data Mining? What is Predictive Analytics?
- Why is this Book Needed?
- Who Will Benefit from this Book?
- Danger! Data Mining is Easy to do Badly
- “White-Box” Approach
- Algorithm Walk-Throughs
- Exciting New Topics
- The R Zone
- Appendix: Data Summarization and Visualization
- The Case Study: Bringing it all Together
- How the Book is Structured
- The Software
- Weka: The Open-Source Alternative
- The Companion Web Site: www.dataminingconsultant.com
- Data Mining and Predictive Analytics as a Textbook
- Acknowledgments
- Part I: Data Preparation
- Chapter 1: An Introduction to Data Mining and Predictive Analytics
- Chapter 2: Data Preprocessing
- 2.1 Why do We Need to Preprocess the Data?
- 2.2 Data Cleaning
- 2.3 Handling Missing Data
- 2.4 Identifying Misclassifications
- 2.5 Graphical Methods for Identifying Outliers
- 2.6 Measures of Center and Spread
- 2.7 Data Transformation
- 2.8 Min–Max Normalization
- 2.9 Z-Score Standardization
- 2.10 Decimal Scaling
- 2.11 Transformations to Achieve Normality
- 2.12 Numerical Methods for Identifying Outliers
- 2.13 Flag Variables
- 2.14 Transforming Categorical Variables into Numerical Variables
- 2.15 Binning Numerical Variables
- 2.16 Reclassifying Categorical Variables
- 2.17 Adding an Index Field
- 2.18 Removing Variables that are not Useful
- 2.19 Variables that Should Probably not be Removed
- 2.20 Removal of Duplicate Records
- 2.21 A Word About ID Fields
- The R Zone
- R Reference
- Exercises
- Chapter 3: Exploratory Data Analysis
- 3.1 Hypothesis Testing Versus Exploratory Data Analysis
- 3.2 Getting to Know The Data Set
- 3.3 Exploring Categorical Variables
- 3.4 Exploring Numeric Variables
- 3.5 Exploring Multivariate Relationships
- 3.6 Selecting Interesting Subsets of the Data for Further Investigation
- 3.7 Using EDA to Uncover Anomalous Fields
- 3.8 Binning Based on Predictive Value
- 3.9 Deriving New Variables: Flag Variables
- 3.10 Deriving New Variables: Numerical Variables
- 3.11 Using EDA to Investigate Correlated Predictor Variables
- 3.12 Summary of Our EDA
- The R Zone
- R References
- Exercises
- Chapter 4: Dimension-Reduction Methods
- 4.1 Need for Dimension-Reduction in Data Mining
- 4.2 Principal Components Analysis
- 4.3 Applying PCA to the Houses Data Set
- 4.4 How Many Components Should We Extract?
- 4.5 Profiling the Principal Components
- 4.6 Communalities
- 4.7 Validation of the Principal Components
- 4.8 Factor Analysis
- 4.9 Applying Factor Analysis to the Adult Data Set
- 4.10 Factor Rotation
- 4.11 User-Defined Composites
- 4.12 An Example of a User-Defined Composite
- The R Zone
- R References
- Exercises
- Part II: Statistical Analysis
- Chapter 5: Univariate Statistical Analysis
- 5.1 Data Mining Tasks in Discovering Knowledge in Data
- 5.2 Statistical Approaches to Estimation and Prediction
- 5.3 Statistical Inference
- 5.4 How Confident are We in Our Estimates?
- 5.5 Confidence Interval Estimation of the Mean
- 5.6 How to Reduce the Margin of Error
- 5.7 Confidence Interval Estimation of the Proportion
- 5.8 Hypothesis Testing for the Mean
- 5.9 Assessing The Strength of Evidence Against The Null Hypothesis
- 5.10 Using Confidence Intervals to Perform Hypothesis Tests
- 5.11 Hypothesis Testing for The Proportion
- Reference
- The R Zone
- R Reference
- Exercises
- Chapter 6: Multivariate Statistics
- Chapter 7: Preparing to Model the Data
- Chapter 8: Simple Linear Regression
- 8.1 An Example of Simple Linear Regression
- 8.2 Dangers of Extrapolation
- 8.3 How Useful is the Regression? The Coefficient of Determination, 2
- 8.4 Standard Error of the Estimate,
- 8.5 Correlation Coefficient
- 8.6 Anova Table for Simple Linear Regression
- 8.7 Outliers, High Leverage Points, and Influential Observations
- 8.8 Population Regression Equation
- 8.9 Verifying The Regression Assumptions
- 8.10 Inference in Regression
- 8.11 t-Test for the Relationship Between x and y
- 8.12 Confidence Interval for the Slope of the Regression Line
- 8.13 Confidence Interval for the Correlation Coefficient ρ
- 8.14 Confidence Interval for the Mean Value of Given
- 8.15 Prediction Interval for a Randomly Chosen Value of Given
- 8.16 Transformations to Achieve Linearity
- 8.17 Box–Cox Transformations
- The R Zone
- R References
- Exercises
- Chapter 9: Multiple Regression and Model Building
- 9.1 An Example of Multiple Regression
- 9.2 The Population Multiple Regression Equation
- 9.3 Inference in Multiple Regression
- 9.4 Regression With Categorical Predictors, Using Indicator Variables
- 9.5 Adjusting R2: Penalizing Models For Including Predictors That Are Not Useful
- 9.6 Sequential Sums of Squares
- 9.7 Multicollinearity
- 9.8 Variable Selection Methods
- 9.9 Gas Mileage Data Set
- 9.10 An Application of Variable Selection Methods
- 9.11 Using the Principal Components as Predictors in Multiple Regression
- The R Zone
- R References
- Exercises
- Part III: Classification
- Chapter 10: k-Nearest Neighbor Algorithm
- 10.1 Classification Task
- 10.2 k-Nearest Neighbor Algorithm
- 10.3 Distance Function
- 10.4 Combination Function
- 10.5 Quantifying Attribute Relevance: Stretching the Axes
- 10.6 Database Considerations
- 10.7 k-Nearest Neighbor Algorithm for Estimation and Prediction
- 10.8 Choosing k
- 10.9 Application of k-Nearest Neighbor Algorithm Using IBM/SPSS Modeler
- The R Zone
- R References
- Exercises
- Chapter 11: Decision Trees
- Chapter 12: Neural Networks
- 12.1 Input and Output Encoding
- 12.2 Neural Networks for Estimation and Prediction
- 12.3 Simple Example of a Neural Network
- 12.4 Sigmoid Activation Function
- 12.5 Back-Propagation
- 12.6 Gradient-Descent Method
- 12.7 Back-Propagation Rules
- 12.8 Example of Back-Propagation
- 12.9 Termination Criteria
- 12.10 Learning Rate
- 12.11 Momentum Term
- 12.12 Sensitivity Analysis
- 12.13 Application of Neural Network Modeling
- The R Zone
- R References
- Exercises
- Chapter 13: Logistic Regression
- 13.1 Simple Example of Logistic Regression
- 13.2 Maximum Likelihood Estimation
- 13.3 Interpreting Logistic Regression Output
- 13.4 Inference: Are the Predictors Significant?
- 13.5 Odds Ratio and Relative Risk
- 13.6 Interpreting Logistic Regression for a Dichotomous Predictor
- 13.7 Interpreting Logistic Regression for a Polychotomous Predictor
- 13.8 Interpreting Logistic Regression for a Continuous Predictor
- 13.9 Assumption of Linearity
- 13.10 Zero-Cell Problem
- 13.11 Multiple Logistic Regression
- 13.12 Introducing Higher Order Terms to Handle Nonlinearity
- 13.13 Validating the Logistic Regression Model
- 13.14 WEKA: Hands-On Analysis Using Logistic Regression
- The R Zone
- R References
- Exercises
- Chapter 14: NaÏVe Bayes and Bayesian Networks
- 14.1 Bayesian Approach
- 14.2 Maximum A Posteriori (MAP) Classification
- 14.3 Posterior Odds Ratio
- 14.4 Balancing The Data
- 14.5 Naïve Bayes Classification
- 14.6 Interpreting The Log Posterior Odds Ratio
- 14.7 Zero-Cell Problem
- 14.8 Numeric Predictors for Naïve Bayes Classification
- 14.9 WEKA: Hands-on Analysis Using Naïve Bayes
- 14.10 Bayesian Belief Networks
- 14.11 Clothing Purchase Example
- 14.12 Using The Bayesian Network to Find Probabilities
- The R Zone
- R References
- Exercises
- Chapter 15: Model Evaluation Techniques
- 15.1 Model Evaluation Techniques for the Description Task
- 15.2 Model Evaluation Techniques for the Estimation and Prediction Tasks
- 15.3 Model Evaluation Measures for the Classification Task
- 15.4 Accuracy and Overall Error Rate
- 15.5 Sensitivity and Specificity
- 15.6 False-Positive Rate and False-Negative Rate
- 15.7 Proportions of True Positives, True Negatives, False Positives, and False Negatives
- 15.8 Misclassification Cost Adjustment to Reflect Real-World Concerns
- 15.9 Decision Cost/Benefit Analysis
- 15.10 Lift Charts and Gains Charts
- 15.11 Interweaving Model Evaluation with Model Building
- 15.12 Confluence of Results: Applying a Suite of Models
- The R Zone
- R References
- Exercises
- Hands-On Analysis
- Chapter 16: Cost-Benefit Analysis Using Data-Driven Costs
- 16.1 Decision Invariance Under Row Adjustment
- 16.2 Positive Classification Criterion
- 16.3 Demonstration Of The Positive Classification Criterion
- 16.4 Constructing The Cost Matrix
- 16.5 Decision Invariance Under Scaling
- 16.6 Direct Costs and Opportunity Costs
- 16.7 Case Study: Cost-Benefit Analysis Using Data-Driven Misclassification Costs
- 16.8 Rebalancing as a Surrogate for Misclassification Costs
- The R Zone
- R References
- Exercises
- Chapter 17: Cost-Benefit Analysis for Trinary and -Nary Classification Models
- 17.1 Classification Evaluation Measures for a Generic Trinary Target
- 17.2 Application of Evaluation Measures for Trinary Classification to the Loan Approval Problem
- 17.3 Data-Driven Cost-Benefit Analysis for Trinary Loan Classification Problem
- 17.4 Comparing Cart Models With and Without Data-Driven Misclassification Costs
- 17.5 Classification Evaluation Measures for a Generic k-Nary Target
- 17.6 Example of Evaluation Measures and Data-Driven Misclassification Costs for k-Nary Classification
- The R Zone
- R References
- Exercises
- Chapter 18: Graphical Evaluation of Classification Models
- Part IV: Clustering
- Chapter 19: Hierarchical and -Means Clustering
- 19.1 The Clustering Task
- 19.2 Hierarchical Clustering Methods
- 19.3 Single-Linkage Clustering
- 19.4 Complete-Linkage Clustering
- 19.5 -Means Clustering
- 19.6 Example of -Means Clustering at Work
- 19.7 Behavior of MSB, MSE, and Pseudo-F as the -Means Algorithm Proceeds
- 19.8 Application of -Means Clustering Using SAS Enterprise Miner
- 19.9 Using Cluster Membership to Predict Churn
- The R Zone
- R References
- Exercises
- Hands-On Analysis
- Chapter 20: Kohonen Networks
- 20.1 Self-Organizing Maps
- 20.2 Kohonen Networks
- 20.3 Example of a Kohonen Network Study
- 20.4 Cluster Validity
- 20.5 Application of Clustering Using Kohonen Networks
- 20.6 Interpreting The Clusters
- 20.7 Using Cluster Membership as Input to Downstream Data Mining Models
- The R Zone
- R References
- Exercises
- Chapter 21: BIRCH Clustering
- 21.1 Rationale for BIRCH Clustering
- 21.2 Cluster Features
- 21.3 Cluster Feature TREE
- 21.4 Phase 1: Building The CF Tree
- 21.5 Phase 2: Clustering The Sub-Clusters
- 21.6 Example of Birch Clustering, Phase 1: Building The CF Tree
- 21.7 Example of BIRCH Clustering, Phase 2: Clustering The Sub-Clusters
- 21.8 Evaluating The Candidate Cluster Solutions
- 21.9 Case Study: Applying BIRCH Clustering to The Bank Loans Data Set
- The R Zone
- R References
- Exercises
- Chapter 22: Measuring Cluster Goodness
- 22.1 Rationale for Measuring Cluster Goodness
- 22.2 The Silhouette Method
- 22.3 Silhouette Example
- 22.4 Silhouette Analysis of the IRIS Data Set
- 22.5 The Pseudo-F Statistic
- 22.6 Example of the Pseudo-F Statistic
- 22.7 Pseudo-F Statistic Applied to the IRIS Data Set
- 22.8 Cluster Validation
- 22.9 Cluster Validation Applied to the Loans Data Set
- The R Zone
- R References
- Exercises
- Part V: Association Rules
- Chapter 23: Association Rules
- 23.1 Affinity Analysis and Market Basket Analysis
- 23.2 Support, Confidence, Frequent Itemsets, and the A Priori Property
- 23.3 How Does The A Priori Algorithm Work (Part 1)? Generating Frequent Itemsets
- 23.4 How Does The A Priori Algorithm Work (Part 2)? Generating Association Rules
- 23.5 Extension From Flag Data to General Categorical Data
- 23.6 Information-Theoretic Approach: Generalized Rule Induction Method
- 23.7 Association Rules are Easy to do Badly
- 23.8 How Can We Measure the Usefulness of Association Rules?
- 23.9 Do Association Rules Represent Supervised or Unsupervised Learning?
- 23.10 Local Patterns Versus Global Models
- The R Zone
- R References
- Exercises
- Part VI: Enhancing Model Performance
- Chapter 24: Segmentation Models
- Chapter 25: Ensemble Methods: Bagging and Boosting
- Chapter 26: Model Voting and Propensity Averaging
- Part VII: Further Topics
- Chapter 27: Genetic Algorithms
- 27.1 Introduction To Genetic Algorithms
- 27.2 Basic Framework of a Genetic Algorithm
- 27.3 Simple Example of a Genetic Algorithm at Work
- 27.4 Modifications and Enhancements: Selection
- 27.5 Modifications and Enhancements: Crossover
- 27.6 Genetic Algorithms for Real-Valued Variables
- 27.7 Using Genetic Algorithms to Train a Neural Network
- 27.8 WEKA: Hands-On Analysis Using Genetic Algorithms
- The R Zone
- R References
- Chapter 28: Imputation of Missing Data
- Part VIII: Case Study: Predicting Response to Direct-Mail Marketing
- Chapter 29: Case Study, Part 1: Business Understanding, Data Preparation, and EDA
- Chapter 30: Case Study, Part 2: Clustering and Principal Components Analysis
- 30.1 Partitioning the Data
- 30.2 Developing the Principal Components
- 30.3 Validating the Principal Components
- 30.4 Profiling the Principal Components
- 30.5 Choosing the Optimal Number of Clusters Using Birch Clustering
- 30.6 Choosing the Optimal Number of Clusters Using k-Means Clustering
- 30.7 Application of k-Means Clustering
- 30.8 Validating the Clusters
- 30.9 Profiling the Clusters
- Chapter 31: Case Study, Part 3: Modeling And Evaluation For Performance And Interpretability
- 31.1 Do You Prefer The Best Model Performance, Or A Combination Of Performance And Interpretability?
- 31.2 Modeling And Evaluation Overview
- 31.3 Cost-Benefit Analysis Using Data-Driven Costs
- 31.4 Variables to be Input To The Models
- 31.5 Establishing The Baseline Model Performance
- 31.6 Models That Use Misclassification Costs
- 31.7 Models That Need Rebalancing as a Surrogate for Misclassification Costs
- 31.8 Combining Models Using Voting and Propensity Averaging
- 31.9 Interpreting The Most Profitable Model
- Chapter 32: Case Study, Part 4: Modeling and Evaluation for High Performance Only
- Appendix A: Data Summarization and Visualization
- Index
- End User License Agreement
-
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