Hardback : $150.00
Praise for the Second Edition
" full of vivid and thought-provoking anecdotes. needs to be read by anyone with a serious interest in research and marketing. "
Research Magazine
"Shmueli et al.
Foreword xvii Preface to the Third Edition xix Preface to the First Edition xxii Acknowledgments xxiv PART I PRELIMINARIES CHAPTER 1 Introduction 3 1.1 What is Business Analytics? 3 1.2 What is Data Mining? 5 1.3 Data Mining and Related Terms 5 1.4 Big Data 6 1.5 Data Science 7 1.6 Why Are There So Many Different Methods? 8 1.7 Terminology and Notation 9 1.8 Road Maps to This Book 11 Order of Topics 12 CHAPTER 2 Overview of the Data Mining Process 14 2.1 Introduction 14 2.2 Core Ideas in Data Mining 15 2.3 The Steps in Data Mining 18 2.4 Preliminary Steps 20 2.5 Predictive Power and Overfitting 26 2.6 Building a Predictive Model with XLMiner 30 2.7 Using Excel for Data Mining 40 2.8 Automating Data Mining Solutions 40 Data Mining Software Tools (by Herb Edelstein) 42 Problems 45 PART II DATA EXPLORATION AND DIMENSION REDUCTION CHAPTER 3 Data Visualization 50 3.1 Uses of Data Visualization 50 3.2 Data Examples 52 Example 1: Boston Housing Data 52 Example 2: Ridership on Amtrak Trains 53 3.3 Basic Charts: Bar Charts, Line Graphs, and Scatter Plots 53 Distribution Plots 54 Heatmaps: Visualizing Correlations and Missing Values 57 3.4 Multi-Dimensional Visualization 58 Adding Variables 59 Manipulations 61 Reference: trend line and labels 64 Scaling up to Large Datasets 65 Multivariate Plot 66 Interactive Visualization 67 3.5 Specialized Visualizations 70 Visualizing Networked Data 70 Visualizing Hierarchical Data: Treemaps 72 Visualizing Geographical Data: Map Charts 73 3.6 Summary: Major Visualizations and Operations, by Data Mining Goal 75 Prediction 75 Classification 75 Time Series Forecasting 75 Unsupervised Learning 76 Problems 77 CHAPTER 4 Dimension Reduction 79 4.1 Introduction 79 4.2 Curse of Dimensionality 80 4.3 Practical Considerations 80 Example 1: House Prices in Boston 80 4.4 Data Summaries 81 4.5 Correlation Analysis 84 4.6 Reducing the Number of Categories in Categorical Variables 85 4.7 Converting A Categorical Variable to A Numerical Variable 86 4.8 Principal Components Analysis 86 Example 2: Breakfast Cereals 87 Principal Components 92 Normalizing the Data 93 Using Principal Components for Classification and Prediction 94 4.9 Dimension Reduction Using Regression Models 96 4.10 Dimension Reduction Using Classification and Regression Trees 96 Problems 97 PART III PERFORMANCE EVALUATION CHAPTER 5 Evaluating Predictive Performance 101 5.1 Introduction 101 5.2 Evaluating Predictive Performance 102 Benchmark: The Average 102 Prediction Accuracy Measures 103 5.3 Judging Classifier Performance 106 Benchmark: The Naive Rule 107 Class Separation 107 The Classification Matrix 107 Using the Validation Data 109 Accuracy Measures 109 Cutoff for Classification 110 Performance in Unequal Importance of Classes 114 Asymmetric Misclassification Costs 116 5.4 Judging Ranking Performance 119 5.5 Oversampling 123 Problems 129 PART IV PREDICTION AND CLASSIFICATION METHODS CHAPTER 6 Multiple Linear Regression 134 6.1 Introduction 134 6.2 Explanatory vs. Predictive Modeling 135 6.3 Estimating the Regression Equation and Prediction 136 Example: Predicting the Price of Used Toyota Corolla Cars 137 6.4 Variable Selection in Linear Regression 141 Reducing the Number of Predictors 141 How to Reduce the Number of Predictors 142 Problems 147 CHAPTER 7 k-Nearest Neighbors (kNN) 151 7.1 The k-NN Classifier (categorical outcome) 151 Determining Neighbors 151 Classification Rule 152 Example: Riding Mowers 152 Choosing k 154 Setting the Cutoff Value 154 7.2 k-NN for a Numerical Response 156 7.3 Advantages and Shortcomings of k-NN Algorithms 158 Problems 160 CHAPTER 8 The Naive Bayes Classifier 162 8.1 Introduction 162 Example 1: Predicting Fraudulent Financial Reporting 163 8.2 Applying the Full (Exact) Bayesian Classifier 164 8.3 Advantages and Shortcomings of the Naive Bayes Classifier 172 Advantages and Shortcomings of the naive Bayes Classifier 172 Problems 176 CHAPTER 9 Classification and Regression Trees 178 9.1 Introduction 178 9.2 Classification Trees 179 Example 1: Riding Mowers 180 9.3 Measures of Impurity 183 9.4 Evaluating the Performance of a Classification Tree 187 Example 2: Acceptance of Personal Loan 188 9.5 Avoiding Overfitting 192 Stopping Tree Growth: CHAID 192 Pruning the Tree 193 9.6 Classification Rules from Trees 198 9.7 Classification Trees for More Than two Classes 198 9.8 Regression Trees 198 Prediction 199 Measuring Impurity 200 Evaluating Performance 200 9.9 Advantages and Weaknesses of a Tree 200 9.10 Improving Prediction: Multiple Trees 202 Problems 205 CHAPTER 10 Logistic Regression 209 10.1 Introduction 209 10.2 The Logistic Regression Model 211 Example: Acceptance of Personal Loan 212 Model with a Single Predictor 214 Estimating the Logistic Model from Data 215 Interpreting Results in Terms of Odds 218 10.3 Evaluating Classification Performance 219 Variable Selection 220 10.4 Example of Complete Analysis: Predicting Delayed Flights 222 Data Preprocessing 224 Model Fitting and Estimation 224 Model Interpretation 226 Model Performance 226 Variable Selection 227 10.5 Appendix: Logistic Regression for Profiling 231 Appendix A: Why Linear Regression Is Problematic for a Categorical Response 231 Appendix B: Evaluating Explanatory Power 233 Appendix C: Logistic Regression for More Than Two Classes 235 Problems 239 CHAPTER 11 Neural Nets 242 11.1 Introduction 242 11.2 Concept and Structure of a Neural Network 243 11.3 Fitting a Network to Data 243 Example 1: Tiny Dataset 244 Computing Output of Nodes 245 Preprocessing the Data 248 Training the Model 248 Example 2: Classifying Accident Severity 253 Avoiding overfitting 254 Using the Output for Prediction and Classification 258 11.4 Required User Input 258 11.5 Exploring the Relationship Between Predictors and Response 259 11.6 Advantages and Weaknesses of Neural Networks 261 Problems 262 CHAPTER 12 Discriminant Analysis 264 12.1 Introduction 264 Example 1: Riding Mowers 265 Example 2: Personal Loan Acceptance 265 12.2 Distance of an Observation from a Class 267 12.3 Fisher's Linear Classification Functions 268 12.4 Classification Performance of Discriminant Analysis 272 12.5 Prior Probabilities 273 12.6 Unequal Misclassification Costs 274 12.7 Classifying More Than Two Classes 274 Example 3: Medical Dispatch to Accident Scenes 274 12.8 Advantages and Weaknesses 277 Problems 279 CHAPTER 13 Combining Methods: Ensembles and Uplift Modeling 282 13.1 Ensembles 282 Why Ensembles Can Improve Predictive Power 283 Simple Averaging 284 Bagging 286 Boosting 286 Advantages and Weaknesses of Ensembles 286 13.2 Uplift (Persuasion) Modeling 287 A-B Testing 287 Uplift 288 Gathering the Data 288 A Simple Model 289 Modeling Individual Uplift 290 Using the Results of an Uplift Model 292 13.3 Summary 292 Problems 293 PART V MINING RELATIONSHIPS AMONG RECORDS CHAPTER 14 Association Rules and Collaborative Filtering 297 14.1 Association Rules 297 Discovering Association Rules in Transaction Databases 298 Example 1: Purchases of Phone Faceplates 298 Generating Candidate Rules 298 The Apriori Algorithm 301 Selecting Strong Rules 301 Data Format 303 The Process of Rule Selection 304 Interpreting the Results 306 Rules and Chance 306 Example 2: Rules for Similar Book Purchases 308 14.2 Collaborative Filtering1 310 Data Type and Format 311 Example 3: Netflix Prize Contest 311 User-Based Collaborative Filtering: "People Like You" 312 Item-Based Collaborative Filtering 315 Advantages and Weaknesses of Collaborative Filtering 316 Collaborative Filtering vs. Association Rules 316 14.3 Summary 318 Problems 320 CHAPTER 15 Cluster Analysis 324 15.1 Introduction 324 Example: Public Utilities 326 15.2 Measuring Distance Between Two Observations 328 Euclidean Distance 328 Normalizing Numerical Measurements 328 Other Distance Measures for Numerical Data 329 Distance Measures for Categorical Data 331 Distance Measures for Mixed Data 331 15.3 Measuring Distance Between Two Clusters 332 15.4 Hierarchical (Agglomerative) Clustering 334 Single Linkage 335 Complete Linkage 335 Average Linkage 336 Centroid Linkage 336 Dendrograms: Displaying Clustering Process and Results 337 Validating Clusters 339 Limitations of Hierarchical Clustering 340 15.5 Non-hierarchical Clustering: The k-Means Algorithm 341 Initial Partition into k Clusters 342 Problems 346 PART VI FORECASTING TIME SERIES CHAPTER 16 Handling Time Series 351 16.1 Introduction 351 16.2 Descriptive vs. Predictive Modeling 352 16.3 Popular Forecasting Methods in Business 353 Combining Methods 353 16.4 Time Series Components 354 Example: Ridership on Amtrak Trains 354 16.5 Data Partitioning and Performance Evaluation 358 Benchmark Performance: Naive Forecasts 359 Generating Future Forecasts 359 Problems 361 CHAPTER 17 Regression-Based Forecasting 364 17.1 A Model with Trend 364 Linear Trend 364 Exponential Trend 366 Polynomial Trend 369 17.2 A Model with Seasonality 370 17.3 A model with trend and seasonality 371 17.4 Autocorrelation and ARIMA Models 371 Computing Autocorrelation 374 Improving Forecasts by Integrating Autocorrelation Information 376 Evaluating Predictability 380 Problems 382 CHAPTER 18 Smoothing Methods 392 18.1 Introduction 392 18.2 Moving Average 393 Centered Moving Average for Visualization 393 Trailing Moving Average for Forecasting 395 Choosing Window Width (w) 399 18.3 Simple Exponential Smoothing 399 Choosing Smoothing Parameter 400 Relation Between Moving Average and Simple Exponential Smoothing 401 18.4 Advanced Exponential Smoothing 402 Series with a Trend 402 Series with a Trend and Seasonality 403 Series with Seasonality (No Trend) 403 Problems 405 PART VII DATA ANALYTICS CHAPTER 19 Social Network Analytics 415 19.1 Introduction 415 19.2 Directed vs. Undirected Networks 416 19.3 Visualizing and analyzing networks 418 Graph Layout 418 Adjacency List 421 Adjacency Matrix 422 Using Network Data in Classification and Prediction 422 19.4 Social Data Metrics and Taxonomy 423 Node-Level Centrality Metrics 423 Egocentric Network 424 Network Metrics 425 19.5 Using Network Metrics in Prediction and Classification 427 Link Prediction 427 Entity Resolution 427 Collaborative Filtering 428 Advantages and Disadvantages 431 Problems 434 CHAPTER 20 Text Mining 436 20.1 Introduction 436 20.2 The Spreadsheet Representation of Text: "Bag-of-Words" 437 20.3 Bag-of-Words vs. Meaning Extraction at Document Level 437 20.4 Preprocessing the Text 438 Tokenization 439 Text Reduction 439 Presence/Absence vs. Frequency 440 Term Frequency - Inverse Document Frequency (TF-IDF) 441 From Terms to Concepts: Latent Semantic Indexing 441 Extracting Meaning 441 20.5 Implementing data mining methods 442 20.6 Example: Online Discussions on Autos and Electronics 442 Importing and Labeling the Records 443 Tokenization 444 Text Processing and Reduction 444 Producing a Concept Matrix 444 Labeling the Documents 447 Fitting a Model 447 Prediction 449 20.7 Summary 449 Problems 450 PART VIII CASES CHAPTER 21 Cases 454 21.1 Charles Book Club2 454 21.2 German Credit 463 21.3 Tayko Software Cataloger3 468 21.4 Political Persuasion4 472 21.5 Taxi Cancellations5 475 21.6 Segmenting Consumers of Bath Soap6 477 21.7 Direct-Mail Fundraising 480 21.8 Catalog Cross-Selling7 483 21.9 Predicting Bankruptcy 484 21.10Time Series Case: Forecasting Public Transportation Demand 487 References 489
Show more
Praise for the Second Edition
" full of vivid and thought-provoking anecdotes. needs to be read by anyone with a serious interest in research and marketing. "
Research Magazine
"Shmueli et al.
Foreword xvii Preface to the Third Edition xix Preface to the First Edition xxii Acknowledgments xxiv PART I PRELIMINARIES CHAPTER 1 Introduction 3 1.1 What is Business Analytics? 3 1.2 What is Data Mining? 5 1.3 Data Mining and Related Terms 5 1.4 Big Data 6 1.5 Data Science 7 1.6 Why Are There So Many Different Methods? 8 1.7 Terminology and Notation 9 1.8 Road Maps to This Book 11 Order of Topics 12 CHAPTER 2 Overview of the Data Mining Process 14 2.1 Introduction 14 2.2 Core Ideas in Data Mining 15 2.3 The Steps in Data Mining 18 2.4 Preliminary Steps 20 2.5 Predictive Power and Overfitting 26 2.6 Building a Predictive Model with XLMiner 30 2.7 Using Excel for Data Mining 40 2.8 Automating Data Mining Solutions 40 Data Mining Software Tools (by Herb Edelstein) 42 Problems 45 PART II DATA EXPLORATION AND DIMENSION REDUCTION CHAPTER 3 Data Visualization 50 3.1 Uses of Data Visualization 50 3.2 Data Examples 52 Example 1: Boston Housing Data 52 Example 2: Ridership on Amtrak Trains 53 3.3 Basic Charts: Bar Charts, Line Graphs, and Scatter Plots 53 Distribution Plots 54 Heatmaps: Visualizing Correlations and Missing Values 57 3.4 Multi-Dimensional Visualization 58 Adding Variables 59 Manipulations 61 Reference: trend line and labels 64 Scaling up to Large Datasets 65 Multivariate Plot 66 Interactive Visualization 67 3.5 Specialized Visualizations 70 Visualizing Networked Data 70 Visualizing Hierarchical Data: Treemaps 72 Visualizing Geographical Data: Map Charts 73 3.6 Summary: Major Visualizations and Operations, by Data Mining Goal 75 Prediction 75 Classification 75 Time Series Forecasting 75 Unsupervised Learning 76 Problems 77 CHAPTER 4 Dimension Reduction 79 4.1 Introduction 79 4.2 Curse of Dimensionality 80 4.3 Practical Considerations 80 Example 1: House Prices in Boston 80 4.4 Data Summaries 81 4.5 Correlation Analysis 84 4.6 Reducing the Number of Categories in Categorical Variables 85 4.7 Converting A Categorical Variable to A Numerical Variable 86 4.8 Principal Components Analysis 86 Example 2: Breakfast Cereals 87 Principal Components 92 Normalizing the Data 93 Using Principal Components for Classification and Prediction 94 4.9 Dimension Reduction Using Regression Models 96 4.10 Dimension Reduction Using Classification and Regression Trees 96 Problems 97 PART III PERFORMANCE EVALUATION CHAPTER 5 Evaluating Predictive Performance 101 5.1 Introduction 101 5.2 Evaluating Predictive Performance 102 Benchmark: The Average 102 Prediction Accuracy Measures 103 5.3 Judging Classifier Performance 106 Benchmark: The Naive Rule 107 Class Separation 107 The Classification Matrix 107 Using the Validation Data 109 Accuracy Measures 109 Cutoff for Classification 110 Performance in Unequal Importance of Classes 114 Asymmetric Misclassification Costs 116 5.4 Judging Ranking Performance 119 5.5 Oversampling 123 Problems 129 PART IV PREDICTION AND CLASSIFICATION METHODS CHAPTER 6 Multiple Linear Regression 134 6.1 Introduction 134 6.2 Explanatory vs. Predictive Modeling 135 6.3 Estimating the Regression Equation and Prediction 136 Example: Predicting the Price of Used Toyota Corolla Cars 137 6.4 Variable Selection in Linear Regression 141 Reducing the Number of Predictors 141 How to Reduce the Number of Predictors 142 Problems 147 CHAPTER 7 k-Nearest Neighbors (kNN) 151 7.1 The k-NN Classifier (categorical outcome) 151 Determining Neighbors 151 Classification Rule 152 Example: Riding Mowers 152 Choosing k 154 Setting the Cutoff Value 154 7.2 k-NN for a Numerical Response 156 7.3 Advantages and Shortcomings of k-NN Algorithms 158 Problems 160 CHAPTER 8 The Naive Bayes Classifier 162 8.1 Introduction 162 Example 1: Predicting Fraudulent Financial Reporting 163 8.2 Applying the Full (Exact) Bayesian Classifier 164 8.3 Advantages and Shortcomings of the Naive Bayes Classifier 172 Advantages and Shortcomings of the naive Bayes Classifier 172 Problems 176 CHAPTER 9 Classification and Regression Trees 178 9.1 Introduction 178 9.2 Classification Trees 179 Example 1: Riding Mowers 180 9.3 Measures of Impurity 183 9.4 Evaluating the Performance of a Classification Tree 187 Example 2: Acceptance of Personal Loan 188 9.5 Avoiding Overfitting 192 Stopping Tree Growth: CHAID 192 Pruning the Tree 193 9.6 Classification Rules from Trees 198 9.7 Classification Trees for More Than two Classes 198 9.8 Regression Trees 198 Prediction 199 Measuring Impurity 200 Evaluating Performance 200 9.9 Advantages and Weaknesses of a Tree 200 9.10 Improving Prediction: Multiple Trees 202 Problems 205 CHAPTER 10 Logistic Regression 209 10.1 Introduction 209 10.2 The Logistic Regression Model 211 Example: Acceptance of Personal Loan 212 Model with a Single Predictor 214 Estimating the Logistic Model from Data 215 Interpreting Results in Terms of Odds 218 10.3 Evaluating Classification Performance 219 Variable Selection 220 10.4 Example of Complete Analysis: Predicting Delayed Flights 222 Data Preprocessing 224 Model Fitting and Estimation 224 Model Interpretation 226 Model Performance 226 Variable Selection 227 10.5 Appendix: Logistic Regression for Profiling 231 Appendix A: Why Linear Regression Is Problematic for a Categorical Response 231 Appendix B: Evaluating Explanatory Power 233 Appendix C: Logistic Regression for More Than Two Classes 235 Problems 239 CHAPTER 11 Neural Nets 242 11.1 Introduction 242 11.2 Concept and Structure of a Neural Network 243 11.3 Fitting a Network to Data 243 Example 1: Tiny Dataset 244 Computing Output of Nodes 245 Preprocessing the Data 248 Training the Model 248 Example 2: Classifying Accident Severity 253 Avoiding overfitting 254 Using the Output for Prediction and Classification 258 11.4 Required User Input 258 11.5 Exploring the Relationship Between Predictors and Response 259 11.6 Advantages and Weaknesses of Neural Networks 261 Problems 262 CHAPTER 12 Discriminant Analysis 264 12.1 Introduction 264 Example 1: Riding Mowers 265 Example 2: Personal Loan Acceptance 265 12.2 Distance of an Observation from a Class 267 12.3 Fisher's Linear Classification Functions 268 12.4 Classification Performance of Discriminant Analysis 272 12.5 Prior Probabilities 273 12.6 Unequal Misclassification Costs 274 12.7 Classifying More Than Two Classes 274 Example 3: Medical Dispatch to Accident Scenes 274 12.8 Advantages and Weaknesses 277 Problems 279 CHAPTER 13 Combining Methods: Ensembles and Uplift Modeling 282 13.1 Ensembles 282 Why Ensembles Can Improve Predictive Power 283 Simple Averaging 284 Bagging 286 Boosting 286 Advantages and Weaknesses of Ensembles 286 13.2 Uplift (Persuasion) Modeling 287 A-B Testing 287 Uplift 288 Gathering the Data 288 A Simple Model 289 Modeling Individual Uplift 290 Using the Results of an Uplift Model 292 13.3 Summary 292 Problems 293 PART V MINING RELATIONSHIPS AMONG RECORDS CHAPTER 14 Association Rules and Collaborative Filtering 297 14.1 Association Rules 297 Discovering Association Rules in Transaction Databases 298 Example 1: Purchases of Phone Faceplates 298 Generating Candidate Rules 298 The Apriori Algorithm 301 Selecting Strong Rules 301 Data Format 303 The Process of Rule Selection 304 Interpreting the Results 306 Rules and Chance 306 Example 2: Rules for Similar Book Purchases 308 14.2 Collaborative Filtering1 310 Data Type and Format 311 Example 3: Netflix Prize Contest 311 User-Based Collaborative Filtering: "People Like You" 312 Item-Based Collaborative Filtering 315 Advantages and Weaknesses of Collaborative Filtering 316 Collaborative Filtering vs. Association Rules 316 14.3 Summary 318 Problems 320 CHAPTER 15 Cluster Analysis 324 15.1 Introduction 324 Example: Public Utilities 326 15.2 Measuring Distance Between Two Observations 328 Euclidean Distance 328 Normalizing Numerical Measurements 328 Other Distance Measures for Numerical Data 329 Distance Measures for Categorical Data 331 Distance Measures for Mixed Data 331 15.3 Measuring Distance Between Two Clusters 332 15.4 Hierarchical (Agglomerative) Clustering 334 Single Linkage 335 Complete Linkage 335 Average Linkage 336 Centroid Linkage 336 Dendrograms: Displaying Clustering Process and Results 337 Validating Clusters 339 Limitations of Hierarchical Clustering 340 15.5 Non-hierarchical Clustering: The k-Means Algorithm 341 Initial Partition into k Clusters 342 Problems 346 PART VI FORECASTING TIME SERIES CHAPTER 16 Handling Time Series 351 16.1 Introduction 351 16.2 Descriptive vs. Predictive Modeling 352 16.3 Popular Forecasting Methods in Business 353 Combining Methods 353 16.4 Time Series Components 354 Example: Ridership on Amtrak Trains 354 16.5 Data Partitioning and Performance Evaluation 358 Benchmark Performance: Naive Forecasts 359 Generating Future Forecasts 359 Problems 361 CHAPTER 17 Regression-Based Forecasting 364 17.1 A Model with Trend 364 Linear Trend 364 Exponential Trend 366 Polynomial Trend 369 17.2 A Model with Seasonality 370 17.3 A model with trend and seasonality 371 17.4 Autocorrelation and ARIMA Models 371 Computing Autocorrelation 374 Improving Forecasts by Integrating Autocorrelation Information 376 Evaluating Predictability 380 Problems 382 CHAPTER 18 Smoothing Methods 392 18.1 Introduction 392 18.2 Moving Average 393 Centered Moving Average for Visualization 393 Trailing Moving Average for Forecasting 395 Choosing Window Width (w) 399 18.3 Simple Exponential Smoothing 399 Choosing Smoothing Parameter 400 Relation Between Moving Average and Simple Exponential Smoothing 401 18.4 Advanced Exponential Smoothing 402 Series with a Trend 402 Series with a Trend and Seasonality 403 Series with Seasonality (No Trend) 403 Problems 405 PART VII DATA ANALYTICS CHAPTER 19 Social Network Analytics 415 19.1 Introduction 415 19.2 Directed vs. Undirected Networks 416 19.3 Visualizing and analyzing networks 418 Graph Layout 418 Adjacency List 421 Adjacency Matrix 422 Using Network Data in Classification and Prediction 422 19.4 Social Data Metrics and Taxonomy 423 Node-Level Centrality Metrics 423 Egocentric Network 424 Network Metrics 425 19.5 Using Network Metrics in Prediction and Classification 427 Link Prediction 427 Entity Resolution 427 Collaborative Filtering 428 Advantages and Disadvantages 431 Problems 434 CHAPTER 20 Text Mining 436 20.1 Introduction 436 20.2 The Spreadsheet Representation of Text: "Bag-of-Words" 437 20.3 Bag-of-Words vs. Meaning Extraction at Document Level 437 20.4 Preprocessing the Text 438 Tokenization 439 Text Reduction 439 Presence/Absence vs. Frequency 440 Term Frequency - Inverse Document Frequency (TF-IDF) 441 From Terms to Concepts: Latent Semantic Indexing 441 Extracting Meaning 441 20.5 Implementing data mining methods 442 20.6 Example: Online Discussions on Autos and Electronics 442 Importing and Labeling the Records 443 Tokenization 444 Text Processing and Reduction 444 Producing a Concept Matrix 444 Labeling the Documents 447 Fitting a Model 447 Prediction 449 20.7 Summary 449 Problems 450 PART VIII CASES CHAPTER 21 Cases 454 21.1 Charles Book Club2 454 21.2 German Credit 463 21.3 Tayko Software Cataloger3 468 21.4 Political Persuasion4 472 21.5 Taxi Cancellations5 475 21.6 Segmenting Consumers of Bath Soap6 477 21.7 Direct-Mail Fundraising 480 21.8 Catalog Cross-Selling7 483 21.9 Predicting Bankruptcy 484 21.10Time Series Case: Forecasting Public Transportation Demand 487 References 489
Show moreForeword xvii
Preface to the Third Edition xix
Preface to the First Edition xxii
Acknowledgments xxiv
PART I PRELIMINARIES
CHAPTER 1 Introduction 3
1.1 What is Business Analytics? 3
1.2 What is Data Mining? 5
1.3 Data Mining and Related Terms 5
1.4 Big Data 6
1.5 Data Science 7
1.6 Why Are There So Many Different Methods? 8
1.7 Terminology and Notation 9
1.8 Road Maps to This Book 11
Order of Topics 12
CHAPTER 2 Overview of the Data Mining Process 14
2.1 Introduction 14
2.2 Core Ideas in Data Mining 15
2.3 The Steps in Data Mining 18
2.4 Preliminary Steps 20
2.5 Predictive Power and Overfitting 26
2.6 Building a Predictive Model with XLMiner 30
2.7 Using Excel for Data Mining 40
2.8 Automating Data Mining Solutions 40
Data Mining Software Tools (by Herb Edelstein) 42
Problems 45
PART II DATA EXPLORATION AND DIMENSION REDUCTION
CHAPTER 3 Data Visualization 50
3.1 Uses of Data Visualization 50
3.2 Data Examples 52
Example 1: Boston Housing Data 52
Example 2: Ridership on Amtrak Trains 53
3.3 Basic Charts: Bar Charts, Line Graphs, and Scatter Plots 53
Distribution Plots 54
Heatmaps: Visualizing Correlations and Missing Values 57
3.4 Multi-Dimensional Visualization 58
Adding Variables 59
Manipulations 61
Reference: trend line and labels 64
Scaling up to Large Datasets 65
Multivariate Plot 66
Interactive Visualization 67
3.5 Specialized Visualizations 70
Visualizing Networked Data 70
Visualizing Hierarchical Data: Treemaps 72
Visualizing Geographical Data: Map Charts 73
3.6 Summary: Major Visualizations and Operations, by Data Mining Goal 75
Prediction 75
Classification 75
Time Series Forecasting 75
Unsupervised Learning 76
Problems 77
CHAPTER 4 Dimension Reduction 79
4.1 Introduction 79
4.2 Curse of Dimensionality 80
4.3 Practical Considerations 80
Example 1: House Prices in Boston 80
4.4 Data Summaries 81
4.5 Correlation Analysis 84
4.6 Reducing the Number of Categories in Categorical Variables 85
4.7 Converting A Categorical Variable to A Numerical Variable 86
4.8 Principal Components Analysis 86
Example 2: Breakfast Cereals 87
Principal Components 92
Normalizing the Data 93
Using Principal Components for Classification and Prediction 94
4.9 Dimension Reduction Using Regression Models 96
4.10 Dimension Reduction Using Classification and Regression Trees 96
Problems 97
PART III PERFORMANCE EVALUATION
CHAPTER 5 Evaluating Predictive Performance 101
5.1 Introduction 101
5.2 Evaluating Predictive Performance 102
Benchmark: The Average 102
Prediction Accuracy Measures 103
5.3 Judging Classifier Performance 106
Benchmark: The Naive Rule 107
Class Separation 107
The Classification Matrix 107
Using the Validation Data 109
Accuracy Measures 109
Cutoff for Classification 110
Performance in Unequal Importance of Classes 114
Asymmetric Misclassification Costs 116
5.4 Judging Ranking Performance 119
5.5 Oversampling 123
Problems 129
PART IV PREDICTION AND CLASSIFICATION METHODS
CHAPTER 6 Multiple Linear Regression 134
6.1 Introduction 134
6.2 Explanatory vs. Predictive Modeling 135
6.3 Estimating the Regression Equation and Prediction 136
Example: Predicting the Price of Used Toyota Corolla Cars 137
6.4 Variable Selection in Linear Regression 141
Reducing the Number of Predictors 141
How to Reduce the Number of Predictors 142
Problems 147
CHAPTER 7 k-Nearest Neighbors (kNN) 151
7.1 The k-NN Classifier (categorical outcome) 151
Determining Neighbors 151
Classification Rule 152
Example: Riding Mowers 152
Choosing k 154
Setting the Cutoff Value 154
7.2 k-NN for a Numerical Response 156
7.3 Advantages and Shortcomings of k-NN Algorithms 158
Problems 160
CHAPTER 8 The Naive Bayes Classifier 162
8.1 Introduction 162
Example 1: Predicting Fraudulent Financial Reporting 163
8.2 Applying the Full (Exact) Bayesian Classifier 164
8.3 Advantages and Shortcomings of the Naive Bayes Classifier 172
Advantages and Shortcomings of the naive Bayes Classifier 172
Problems 176
CHAPTER 9 Classification and Regression Trees 178
9.1 Introduction 178
9.2 Classification Trees 179
Example 1: Riding Mowers 180
9.3 Measures of Impurity 183
9.4 Evaluating the Performance of a Classification Tree 187
Example 2: Acceptance of Personal Loan 188
9.5 Avoiding Overfitting 192
Stopping Tree Growth: CHAID 192
Pruning the Tree 193
9.6 Classification Rules from Trees 198
9.7 Classification Trees for More Than two Classes 198
9.8 Regression Trees 198
Prediction 199
Measuring Impurity 200
Evaluating Performance 200
9.9 Advantages and Weaknesses of a Tree 200
9.10 Improving Prediction: Multiple Trees 202
Problems 205
CHAPTER 10 Logistic Regression 209
10.1 Introduction 209
10.2 The Logistic Regression Model 211
Example: Acceptance of Personal Loan 212
Model with a Single Predictor 214
Estimating the Logistic Model from Data 215
Interpreting Results in Terms of Odds 218
10.3 Evaluating Classification Performance 219
Variable Selection 220
10.4 Example of Complete Analysis: Predicting Delayed Flights 222
Data Preprocessing 224
Model Fitting and Estimation 224
Model Interpretation 226
Model Performance 226
Variable Selection 227
10.5 Appendix: Logistic Regression for Profiling 231
Appendix A: Why Linear Regression Is Problematic for a Categorical Response 231
Appendix B: Evaluating Explanatory Power 233
Appendix C: Logistic Regression for More Than Two Classes 235
Problems 239
CHAPTER 11 Neural Nets 242
11.1 Introduction 242
11.2 Concept and Structure of a Neural Network 243
11.3 Fitting a Network to Data 243
Example 1: Tiny Dataset 244
Computing Output of Nodes 245
Preprocessing the Data 248
Training the Model 248
Example 2: Classifying Accident Severity 253
Avoiding overfitting 254
Using the Output for Prediction and Classification 258
11.4 Required User Input 258
11.5 Exploring the Relationship Between Predictors and Response 259
11.6 Advantages and Weaknesses of Neural Networks 261
Problems 262
CHAPTER 12 Discriminant Analysis 264
12.1 Introduction 264
Example 1: Riding Mowers 265
Example 2: Personal Loan Acceptance 265
12.2 Distance of an Observation from a Class 267
12.3 Fisher’s Linear Classification Functions 268
12.4 Classification Performance of Discriminant Analysis 272
12.5 Prior Probabilities 273
12.6 Unequal Misclassification Costs 274
12.7 Classifying More Than Two Classes 274
Example 3: Medical Dispatch to Accident Scenes 274
12.8 Advantages and Weaknesses 277
Problems 279
CHAPTER 13 Combining Methods: Ensembles and Uplift Modeling 282
13.1 Ensembles 282
Why Ensembles Can Improve Predictive Power 283
Simple Averaging 284
Bagging 286
Boosting 286
Advantages and Weaknesses of Ensembles 286
13.2 Uplift (Persuasion) Modeling 287
A-B Testing 287
Uplift 288
Gathering the Data 288
A Simple Model 289
Modeling Individual Uplift 290
Using the Results of an Uplift Model 292
13.3 Summary 292
Problems 293
PART V MINING RELATIONSHIPS AMONG RECORDS
CHAPTER 14 Association Rules and Collaborative Filtering 297
14.1 Association Rules 297
Discovering Association Rules in Transaction Databases 298
Example 1: Purchases of Phone Faceplates 298
Generating Candidate Rules 298
The Apriori Algorithm 301
Selecting Strong Rules 301
Data Format 303
The Process of Rule Selection 304
Interpreting the Results 306
Rules and Chance 306
Example 2: Rules for Similar Book Purchases 308
14.2 Collaborative Filtering1 310
Data Type and Format 311
Example 3: Netflix Prize Contest 311
User-Based Collaborative Filtering: “People Like You” 312
Item-Based Collaborative Filtering 315
Advantages and Weaknesses of Collaborative Filtering 316
Collaborative Filtering vs. Association Rules 316
14.3 Summary 318
Problems 320
CHAPTER 15 Cluster Analysis 324
15.1 Introduction 324
Example: Public Utilities 326
15.2 Measuring Distance Between Two Observations 328
Euclidean Distance 328
Normalizing Numerical Measurements 328
Other Distance Measures for Numerical Data 329
Distance Measures for Categorical Data 331
Distance Measures for Mixed Data 331
15.3 Measuring Distance Between Two Clusters 332
15.4 Hierarchical (Agglomerative) Clustering 334
Single Linkage 335
Complete Linkage 335
Average Linkage 336
Centroid Linkage 336
Dendrograms: Displaying Clustering Process and Results 337
Validating Clusters 339
Limitations of Hierarchical Clustering 340
15.5 Non-hierarchical Clustering: The k-Means Algorithm 341
Initial Partition into k Clusters 342
Problems 346
PART VI FORECASTING TIME SERIES
CHAPTER 16 Handling Time Series 351
16.1 Introduction 351
16.2 Descriptive vs. Predictive Modeling 352
16.3 Popular Forecasting Methods in Business 353
Combining Methods 353
16.4 Time Series Components 354
Example: Ridership on Amtrak Trains 354
16.5 Data Partitioning and Performance Evaluation 358
Benchmark Performance: Naive Forecasts 359
Generating Future Forecasts 359
Problems 361
CHAPTER 17 Regression-Based Forecasting 364
17.1 A Model with Trend 364
Linear Trend 364
Exponential Trend 366
Polynomial Trend 369
17.2 A Model with Seasonality 370
17.3 A model with trend and seasonality 371
17.4 Autocorrelation and ARIMA Models 371
Computing Autocorrelation 374
Improving Forecasts by Integrating Autocorrelation Information 376
Evaluating Predictability 380
Problems 382
CHAPTER 18 Smoothing Methods 392
18.1 Introduction 392
18.2 Moving Average 393
Centered Moving Average for Visualization 393
Trailing Moving Average for Forecasting 395
Choosing Window Width (w) 399
18.3 Simple Exponential Smoothing 399
Choosing Smoothing Parameter 400
Relation Between Moving Average and Simple Exponential Smoothing 401
18.4 Advanced Exponential Smoothing 402
Series with a Trend 402
Series with a Trend and Seasonality 403
Series with Seasonality (No Trend) 403
Problems 405
PART VII DATA ANALYTICS
CHAPTER 19 Social Network Analytics 415
19.1 Introduction 415
19.2 Directed vs. Undirected Networks 416
19.3 Visualizing and analyzing networks 418
Graph Layout 418
Adjacency List 421
Adjacency Matrix 422
Using Network Data in Classification and Prediction 422
19.4 Social Data Metrics and Taxonomy 423
Node-Level Centrality Metrics 423
Egocentric Network 424
Network Metrics 425
19.5 Using Network Metrics in Prediction and Classification 427
Link Prediction 427
Entity Resolution 427
Collaborative Filtering 428
Advantages and Disadvantages 431
Problems 434
CHAPTER 20 Text Mining 436
20.1 Introduction 436
20.2 The Spreadsheet Representation of Text: “Bag-of-Words” 437
20.3 Bag-of-Words vs. Meaning Extraction at Document Level 437
20.4 Preprocessing the Text 438
Tokenization 439
Text Reduction 439
Presence/Absence vs. Frequency 440
Term Frequency - Inverse Document Frequency (TF-IDF) 441
From Terms to Concepts: Latent Semantic Indexing 441
Extracting Meaning 441
20.5 Implementing data mining methods 442
20.6 Example: Online Discussions on Autos and Electronics 442
Importing and Labeling the Records 443
Tokenization 444
Text Processing and Reduction 444
Producing a Concept Matrix 444
Labeling the Documents 447
Fitting a Model 447
Prediction 449
20.7 Summary 449
Problems 450
PART VIII CASES
CHAPTER 21 Cases 454
21.1 Charles Book Club2 454
21.2 German Credit 463
21.3 Tayko Software Cataloger3 468
21.4 Political Persuasion4 472
21.5 Taxi Cancellations5 475
21.6 Segmenting Consumers of Bath Soap6 477
21.7 Direct-Mail Fundraising 480
21.8 Catalog Cross-Selling7 483
21.9 Predicting Bankruptcy 484
21.10Time Series Case: Forecasting Public Transportation Demand 487
References 489
Galit Shmueli, PhD, is Distinguished Professor at National Tsing Hua University’s Institute of Service Science. She has designed and instructed data mining courses since 2004 at University of Maryland, Statistics.com, The Indian School of Business, and National Tsing Hua University, Taiwan. Professor Shmueli is known for her research and teaching in business analytics, with a focus on statistical and data mining methods in information systems and healthcare. She has authored over 70 journal articles, books, textbooks and book chapters.
Peter C. Bruce is President and Founder of the Institute for Statistics Education at www.statistics.com. He has written multiple journal articles and is the developer of Resampling Stats software. He is the author of Introductory Statistics and Analytics: A Resampling Perspective, also published by Wiley.
Nitin R. Patel, PhD, is Chairman and cofounder of Cytel, Inc., based in Cambridge, Massachusetts. A Fellow of the American Statistical Association, Dr. Patel has also served as a Visiting Professor at the Massachusetts Institute of Technology and at Harvard University. He is a Fellow of the Computer Society of India and was a professor at the Indian Institute of Management, Ahmedabad for 15 years.
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