Preface to the third edition Preface to the first edition Acknowledgments

Transcription

1 Contents Foreword Preface to the third edition Preface to the first edition Acknowledgments Part I PRELIMINARIES XXI XXIII XXVII XXIX CHAPTER 1 Introduction What Is Business Analytics? What Is Data Mining? Data Mining and Related Terms Big Data Data Science Why Are There So Many Different Methods? Terminology and Notation Road Maps to This Book Order of Topics CHAPTER 2 Overview of the Data Mining Process Introduction Core Ideas in Data Mining Classification Prediction Association Rules and Recommendation Systems Predictive Analytics Data Reduction and Dimension Reduction Data Exploration and Visualization Supervised and Unsupervised Learning The Steps in Data Mining Preliminary Steps Organization of Datasets Sampling from a Database Oversampling Rare Events in Classification Tasks COPYRIGHTED MATERIAL ix

2 x CONTENTS Preprocessing and Cleaning the Data Predictive Power and Overfitting Creation and Use of Data Partitions Overfitting Building a Predictive Model with XLMiner Predicting Home Values in the West Roxbury Neighborhood 32 Modeling Process Using Excel for Data Mining Automating Data Mining Solutions Data Mining Software Tools: the State of the Market (by Herb Edelstein) Problems Part II DATA EXPLORATION AND DIMENSION REDUCTION CHAPTER 3 Data Visualization Uses of Data Visualization Data Examples Example 1: Boston Housing Data Example 2: Ridership on Amtrak Trains Basic Charts: Bar Charts, Line Graphs, and Scatter Plots Distribution Plots: Boxplots and Histograms Heatmaps: Visualizing Correlations and Missing Values Multidimensional Visualization Adding Variables: Color, Size, Shape, Multiple Panels, and Animation Manipulations: Re-scaling, Aggregation and Hierarchies, Zooming, Filtering Reference: Trend Line and Labels Scaling up to Large Datasets Multivariate Plot: Parallel Coordinates Plot Interactive Visualization Specialized Visualizations Visualizing Networked Data Visualizing Hierarchical Data: Treemaps Visualizing Geographical Data: Map Charts Summary: Major Visualizations and Operations, by Data Mining Goal Prediction Classification Time Series Forecasting Unsupervised Learning Problems

3 CONTENTS xi CHAPTER 4 Dimension Reduction Introduction Curse of Dimensionality Practical Considerations Example 1: House Prices in Boston Data Summaries Summary Statistics Pivot Tables Correlation Analysis Reducing the Number of Categories in Categorical Variables Converting a Categorical Variable to a Numerical Variable Principal Components Analysis Example 2: Breakfast Cereals Principal Components Normalizing the Data Using Principal Components for Classification and Prediction Dimension Reduction Using Regression Models Dimension Reduction Using Classification and Regression Trees Problems Part III PERFORMANCE EVALUATION CHAPTER 5 Evaluating Predictive Performance Introduction Evaluating Predictive Performance Benchmark: The Average Prediction Accuracy Measures Comparing Training and Validation Performance Lift Chart Judging Classifier Performance Benchmark: The Naive Rule Class Separation The Classification Matrix Using the Validation Data Accuracy Measures Propensities and Cutoff for Classification Performance in Unequal Importance of Classes Asymmetric Misclassification Costs Generalization to More Than Two Classes Judging Ranking Performance Lift Charts for Binary Data

4 xii CONTENTS Decile Lift Charts Beyond Two Classes Lift Charts Incorporating Costs and Benefits Lift as Function of Cutoff Oversampling Oversampling the Training Set Evaluating Model Performance Using a Non-oversampled Validation Set Evaluating Model Performance If Only Oversampled Validation Set Exists Problems Part IV PREDICTION AND CLASSIFICATION METHODS CHAPTER 6 Multiple Linear Regression Introduction Explanatory vs. Predictive Modeling Estimating the Regression Equation and Prediction Example: Predicting the Price of Used Toyota Corolla Cars Variable Selection in Linear Regression Reducing the Number of Predictors How to Reduce the Number of Predictors Problems CHAPTER 7 k-nearest-neighbors (k-nn) The k-nn Classifier (categorical outcome) Determining Neighbors Classification Rule Example: Riding Mowers Choosing k Setting the Cutoff Value k-nn with More Than Two Classes Converting Categorical Variables to Binary Dummies k-nn for a Numerical Response Advantages and Shortcomings of k-nn Algorithms Problems CHAPTER 8 The Naive Bayes Classifier Introduction Cutoff Probability Method Conditional Probability Example 1: Predicting Fraudulent Financial Reporting Applying the Full (Exact) Bayesian Classifier Using the Assign to the Most Probable Class Method.. 172

5 CONTENTS xiii Using the Cutoff Probability Method Practical Difficulty with the Complete (Exact) Bayes Procedure 172 Solution: Naive Bayes Example 2: Predicting Fraudulent Financial Reports, Two Predictors Example 3: Predicting Delayed Flights Advantages and Shortcomings of the Naive Bayes Classifier Problems CHAPTER 9 Classification and Regression Trees Introduction Classification Trees Recursive Partitioning Example 1: Riding Mowers Measures of Impurity Tree Structure Classifying a New Observation Evaluating the Performance of a Classification Tree Example 2: Acceptance of Personal Loan Avoiding Overfitting Stopping Tree Growth: CHAID Pruning the Tree Classification Rules from Trees Classification Trees for More Than two Classes Regression Trees Prediction Measuring Impurity Evaluating Performance Advantages, Weaknesses and Extensions Improving Prediction: Multiple Trees Problems CHAPTER 10 Logistic Regression Introduction The Logistic Regression Model Example: Acceptance of Personal Loan Model with a Single Predictor Estimating the Logistic Model from Data: Computing Parameter Estimates Interpreting Results in Terms of Odds (for a Profiling Goal) Evaluating Classification Performance Variable Selection

6 xiv CONTENTS 10.4 Example of Complete Analysis: Predicting Delayed Flights Data Preprocessing Model Fitting and Estimation Model Interpretation Model Performance Variable Selection Appendix: Logistic Regression for Profiling Appendix A: Why Linear Regression Is Problematic for a Categorical Response Appendix B: Evaluating Explanatory Power Appendix C: Logistic Regression for More Than Two Classes 244 Problems CHAPTER 11 Neural Nets Introduction Concept and Structure of a Neural Network Fitting a Network to Data Example 1: Tiny Dataset Computing Output of Nodes Preprocessing the Data Training the Model Example 2: Classifying Accident Severity Avoiding Overfitting Using the Output for Prediction and Classification Required User Input Exploring the Relationship Between Predictors and Response Unsupervised Feature Extraction and Deep Learning Advantages and Weaknesses of Neural Networks Problems CHAPTER 12 Discriminant Analysis Introduction Example 1: Riding Mowers Example 2: Personal Loan Acceptance Distance of an Observation from a Class Fisher s Linear Classification Functions Classification Performance of Discriminant Analysis Prior Probabilities Unequal Misclassification Costs Classifying More Than Two Classes

7 CONTENTS xv Example 3: Medical Dispatch to Accident Scenes Advantages and Weaknesses Problems CHAPTER 13 Combining Methods: Ensembles and Uplift Modeling Ensembles Why Ensembles Can Improve Predictive Power Simple Averaging Bagging Boosting Advantages and Weaknesses of Ensembles Uplift (Persuasion) Modeling A-B Testing Uplift Gathering the Data A Simple Model Modeling Individual Uplift Using the Results of an Uplift Model Summary Problems Part V MINING RELATIONSHIPS AMONG RECORDS CHAPTER 14 Association Rules and Collaborative Filtering Association Rules Discovering Association Rules in Transaction Databases Example 1: Synthetic Data on Purchases of Phone Faceplates 309 Generating Candidate Rules The Apriori Algorithm Selecting Strong Rules Data Format The Process of Rule Selection Interpreting the Results Rules and Chance Example 2: Rules for Similar Book Purchases Collaborative Filtering Data Type and Format Example 3: Netflix Prize Contest User-Based Collaborative Filtering: People Like You Item-Based Collaborative Filtering Advantages and Weaknesses of Collaborative Filtering Collaborative Filtering vs. Association Rules Summary Problems

8 xvi CONTENTS CHAPTER 15 Cluster Analysis Introduction Example: Public Utilities Measuring Distance Between Two Observations Euclidean Distance Normalizing Numerical Measurements Other Distance Measures for Numerical Data Distance Measures for Categorical Data Distance Measures for Mixed Data Measuring Distance Between Two Clusters Minimum Distance Maximum Distance Average Distance Centroid Distance Hierarchical (Agglomerative) Clustering Single Linkage Complete Linkage Average Linkage (in XLMiner: Group Average Linkage ) Centroid Linkage Ward s Method Dendrograms: Displaying Clustering Process and Results Validating Clusters Limitations of Hierarchical Clustering Non-hierarchical Clustering: The k-means Algorithm Initial Partition into k Clusters Problems Part VI FORECASTING TIME SERIES CHAPTER 16 Handling Time Series Introduction Descriptive vs. Predictive Modeling Popular Forecasting Methods in Business Combining Methods Time Series Components Example: Ridership on Amtrak Trains Data Partitioning and Performance Evaluation Benchmark Performance: Naive Forecasts Generating Future Forecasts Problems

9 CONTENTS xvii CHAPTER 17 Regression-Based Forecasting A Model with Trend Linear Trend Exponential Trend Polynomial Trend A Model with Seasonality A Model with Trend and Seasonality Autocorrelation and ARIMA Models Computing Autocorrelation Improving Forecasts by Integrating Autocorrelation Information Evaluating Predictability Problems CHAPTER 18 Smoothing Methods Introduction Moving Average Centered Moving Average for Visualization Trailing Moving Average for Forecasting Choosing Window Width (w) Simple Exponential Smoothing Choosing Smoothing Parameter α Relation between Moving Average and Simple Exponential Smoothing Advanced Exponential Smoothing Series with a Trend Series with a Trend and Seasonality Series with Seasonality (No Trend) Problems Part VII DATA ANALYTICS CHAPTER 19 Social Network Analytics Introduction Directed vs. Undirected Networks Visualizing and Analyzing Networks Graph Layout Adjacency List Adjacency Matrix Using Network Data in Classification and Prediction Social Data Metrics and Taxonomy Node-Level Centrality Metrics Egocentric Network

10 xviii CONTENTS Network Metrics Using Network Metrics in Prediction and Classification Link Prediction Entity Resolution Collaborative Filtering Advantages and Disadvantages Problems CHAPTER 20 Text Mining Introduction The Spreadsheet Representation of Text: Bag-of-Words Bag-of-Words vs. Meaning Extraction at Document Level Preprocessing the Text Tokenization Text Reduction Presence/Absence vs. Frequency Term Frequency---Inverse Document Frequency (TF-IDF) From Terms to Concepts: Latent Semantic Indexing Extracting Meaning Implementing Data Mining Methods Example: Online Discussions on Autos and Electronics Importing and Labeling the Records Tokenization Text Processing and Reduction Producing a Concept Matrix Labeling the Documents Fitting a Model Prediction Summary Problems Part VIII CASES CHAPTER 21 Cases Charles Book Club The Book Industry Database Marketing at Charles Data Mining Techniques Assignment German Credit Background Data Assignment Tayko Software Cataloger

11 CONTENTS xix Background The Mailing Experiment Data Assignment Political Persuasion Background Predictive Analytics Arrives in US Politics Political Targeting Uplift Data Assignment Taxi Cancellations Business Situation Assignment Segmenting Consumers of Bath Soap Business Situation Key Problems Data Measuring Brand Loyalty Assignment Appendix Direct-Mail Fundraising Background Data Assignment Catalog Cross-Selling Background Assignment Predicting Bankruptcy Predicting Corporate Bankruptcy Assignment Time Series Case: Forecasting Public Transportation Demand 502 Background Problem Description Available Data Assignment Goal Assignment Tips and Suggested Steps References 504 Data Files Used in the Book 506 Index 508

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