Data Mining for Business Analytics

양장본 Hardcover

Galit Shmueli , Peter C. Bruce , Inbal Yahav , Nitin R. Patel , Kenneth C. Lichendahl Jr. 저자(글)

Wiley · 2018년 01월 01일

책 소개

작가정보

저자(글) Galit Shmueli

저자(글) Peter C. Bruce

저자(글) Inbal Yahav

저자(글) Nitin R. Patel

저자(글) Kenneth C. Lichendahl Jr.

목차

• Foreword by Gareth James xix
  Foreword by Ravi Bapna xxi
  Preface to the R Edition xxiii
  Acknowledgments xxvii
  
  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 11
  
  CHAPTER 2 Overview of the Data Mining Process 15
  2.1 Introduction 15
  2.2 Core Ideas in Data Mining 16
  Classification 16
  Prediction 16
  Association Rules and Recommendation Systems 16
  Predictive Analytics 17
  Data Reduction and Dimension Reduction 17
  Data Exploration and Visualization 17
  Supervised and Unsupervised Learning 18
  2.3 The Steps in Data Mining 19
  2.4 Preliminary Steps 21
  Organization of Datasets 21
  Predicting Home Values in the West Roxbury Neighborhood 21
  Loading and Looking at the Data in R 22
  Sampling from a Database 24
  Oversampling Rare Events in Classification Tasks 25
  Preprocessing and Cleaning the Data 26
  2.5 Predictive Power and Overfitting 33
  Overfitting 33
  Creation and Use of Data Partitions 35
  2.6 Building a Predictive Model 38
  Modeling Process 39
  2.7 Using R for Data Mining on a Local Machine 43
  2.8 Automating Data Mining Solutions 43
  Data Mining Software: The State of the Market (by Herb Edelstein) 45
  Problems 49
  
  PART II DATA EXPLORATION AND DIMENSION REDUCTION
  CHAPTER 3 Data Visualization 55
  3.1 Uses of Data Visualization 55
  Base R or ggplot? 57
  3.2 Data Examples 57
  Example 1: Boston Housing Data 57
  Example 2: Ridership on Amtrak Trains 59
  3.3 Basic Charts: Bar Charts, Line Graphs, and Scatter Plots 59
  Distribution Plots: Boxplots and Histograms 61
  Heatmaps: Visualizing Correlations and Missing Values 64
  3.4 Multidimensional Visualization 67
  Adding Variables: Color, Size, Shape, Multiple Panels, and Animation 67
  Manipulations: Rescaling, Aggregation and Hierarchies, Zooming, Filtering 70
  Reference: Trend Lines and Labels 74
  Scaling up to Large Datasets 74
  Multivariate Plot: Parallel Coordinates Plot 75
  Interactive Visualization 77
  3.5 Specialized Visualizations 80
  Visualizing Networked Data 80
  Visualizing Hierarchical Data: Treemaps 82
  Visualizing Geographical Data: Map Charts 83
  3.6 Summary: Major Visualizations and Operations, by Data Mining Goal 86
  Prediction 86
  Classification 86
  Time Series Forecasting 86
  Unsupervised Learning 87
  Problems 88
  
  CHAPTER 4 Dimension Reduction 91
  4.1 Introduction 91
  4.2 Curse of Dimensionality 92
  4.3 Practical Considerations 92
  Example 1: House Prices in Boston 93
  4.4 Data Summaries 94
  Summary Statistics 94
  Aggregation and Pivot Tables 96
  4.5 Correlation Analysis 97
  4.6 Reducing the Number of Categories in Categorical Variables 99
  4.7 Converting a Categorical Variable to a Numerical Variable 99
  4.8 Principal Components Analysis 101
  Example 2: Breakfast Cereals 101
  Principal Components 106
  Normalizing the Data 107
  Using Principal Components for Classification and Prediction 109
  4.9 Dimension Reduction Using Regression Models 111
  4.10 Dimension Reduction Using Classification and Regression Trees 111
  Problems 112
  
  PART III PERFORMANCE EVALUATION
  CHAPTER 5 Evaluating Predictive Performance 117
  5.1 Introduction 117
  5.2 Evaluating Predictive Performance 118
  Naive Benchmark: The Average 118
  Prediction Accuracy Measures 119
  Comparing Training and Validation Performance 121
  Lift Chart 121
  5.3 Judging Classifier Performance 122
  Benchmark: The Naive Rule 124
  Class Separation 124
  The Confusion (Classification) Matrix 124
  Using the Validation Data 126
  Accuracy Measures 126
  Propensities and Cutoff for Classification 127
  Performance in Case of Unequal Importance of Classes 131
  Asymmetric Misclassification Costs 133
  Generalization to More Than Two Classes 135
  5.4 Judging Ranking Performance 136
  Lift Charts for Binary Data 136
  Decile Lift Charts 138
  Beyond Two Classes 139
  Lift Charts Incorporating Costs and Benefits 139
  Lift as a Function of Cutoff 140
  5.5 Oversampling 140
  Oversampling the Training Set 144
  Evaluating Model Performance Using a Non-oversampled Validation Set 144
  Evaluating Model Performance if Only Oversampled Validation Set Exists 144
  Problems 147
  
  PART IV PREDICTION AND CLASSIFICATION METHODS
  CHAPTER 6 Multiple Linear Regression 153
  6.1 Introduction 153
  6.2 Explanatory vs. Predictive Modeling 154
  6.3 Estimating the Regression Equation and Prediction 156
  Example: Predicting the Price of Used Toyota Corolla Cars 156
  6.4 Variable Selection in Linear Regression 161
  Reducing the Number of Predictors 161
  How to Reduce the Number of Predictors 162
  Problems 169
  
  CHAPTER 7 k-Nearest Neighbors (kNN) 173
  7.1 The k-NN Classifier (Categorical Outcome) 173
  Determining Neighbors 173
  Classification Rule 174
  Example: Riding Mowers 175
  Choosing k 176
  Setting the Cutoff Value 179
  k-NN with More Than Two Classes 180
  Converting Categorical Variables to Binary Dummies 180
  7.2 k-NN for a Numerical Outcome 180
  7.3 Advantages and Shortcomings of k-NN Algorithms 182
  Problems 184
  
  CHAPTER 8 The Naive Bayes Classifier 187
  8.1 Introduction 187
  Cutoff Probability Method 188
  Conditional Probability 188
  Example 1: Predicting Fraudulent Financial Reporting 188
  8.2 Applying the Full (Exact) Bayesian Classifier 189
  Using the "Assign to the Most Probable Class" Method 190
  Using the Cutoff Probability Method 190
  Practical Difficulty with the Complete (Exact) Bayes Procedure 190
  Solution: Naive Bayes 191
  The Naive Bayes Assumption of Conditional Independence 192
  Using the Cutoff Probability Method 192
  Example 2: Predicting Fraudulent Financial Reports, Two Predictors 193
  Example 3: Predicting Delayed Flights 194
  8.3 Advantages and Shortcomings of the Naive Bayes Classifier 199
  Problems 202
  
  CHAPTER 9 Classification and Regression Trees 205
  9.1 Introduction 205
  9.2 Classification Trees 207
  Recursive Partitioning 207
  Example 1: Riding Mowers 207
  Measures of Impurity 210
  Tree Structure 214
  Classifying a New Record 214
  9.3 Evaluating the Performance of a Classification Tree 215
  Example 2: Acceptance of Personal Loan 215
  9.4 Avoiding Overfitting 216
  Stopping Tree Growth: Conditional Inference Trees 221
  Pruning the Tree 222
  Cross-Validation 222
  Best-Pruned Tree 224
  9.5 Classification Rules from Trees 226
  9.6 Classification Trees for More Than Two Classes 227
  9.7 Regression Trees 227
  Prediction 228
  Measuring Impurity 228
  Evaluating Performance 229
  9.8 Improving Prediction: Random Forests and Boosted Trees 229
  Random Forests 229
  Boosted Trees 231
  9.9 Advantages and Weaknesses of a Tree 232
  Problems 234
  
  CHAPTER 10 Logistic Regression 237
  10.1 Introduction 237
  10.2 The Logistic Regression Model 239
  10.3 Example: Acceptance of Personal Loan 240
  Model with a Single Predictor 241
  Estimating the Logistic Model from Data: Computing Parameter Estimates 243
  Interpreting Results in Terms of Odds (for a Profiling Goal) 244
  10.4 Evaluating Classification Performance 247
  Variable Selection 248
  10.5 Example of Complete Analysis: Predicting Delayed Flights 250
  Data Preprocessing 251
  Model-Fitting and Estimation 254
  Model Interpretation 254
  Model Performance 254
  Variable Selection 257
  10.6 Appendix: Logistic Regression for Profiling 259
  Appendix A: Why Linear Regression Is Problematic for a Categorical Outcome 259
  Appendix B: Evaluating Explanatory Power 261
  Appendix C: Logistic Regression for More Than Two Classes 264
  Problems 268
  
  CHAPTER 11 Neural Nets 271
  11.1 Introduction 271
  11.2 Concept and Structure of a Neural Network 272
  11.3 Fitting a Network to Data 273
  Example 1: Tiny Dataset 273
  Computing Output of Nodes 274
  Preprocessing the Data 277
  Training the Model 278
  Example 2: Classifying Accident Severity 282
  Avoiding Overfitting 283
  Using the Output for Prediction and Classification 283
  11.4 Required User Input 285
  11.5 Exploring the Relationship Between Predictors and Outcome 287
  11.6 Advantages and Weaknesses of Neural Networks 288
  Problems 290
  
  CHAPTER 12 Discriminant Analysis 293
  12.1 Introduction 293
  Example 1: Riding Mowers 294
  Example 2: Personal Loan Acceptance 294
  12.2 Distance of a Record from a Class 296
  12.3 Fisher's Linear Classification Functions 297
  12.4 Classification Performance of Discriminant Analysis 300
  12.5 Prior Probabilities 302
  12.6 Unequal Misclassification Costs 302
  12.7 Classifying More Than Two Classes 303
  Example 3: Medical Dispatch to Accident Scenes 303
  12.8 Advantages and Weaknesses 306
  Problems 307
  
  CHAPTER 13 Combining Methods: Ensembles and Uplift Modeling 311
  13.1 Ensembles 311
  Why Ensembles Can Improve Predictive Power 312
  Simple Averaging 314
  Bagging 315
  Boosting 315
  Bagging and Boosting in R 315
  Advantages and Weaknesses of Ensembles 315
  13.2 Uplift (Persuasion) Modeling 317
  A-B Testing 318
  Uplift 318
  Gathering the Data 319
  A Simple Model 320
  Modeling Individual Uplift 321
  Computing Uplift with R 322
  Using the Results of an Uplift Model 322
  13.3 Summary 324
  Problems 325
  
  PART V MINING RELATIONSHIPS AMONG RECORDS
  CHAPTER 14 Association Rules and Collaborative Filtering 329
  14.1 Association Rules 329
  Discovering Association Rules in Transaction Databases 330
  Example 1: Synthetic Data on Purchases of Phone Faceplates 330
  Generating Candidate Rules 330
  The Apriori Algorithm 333
  Selecting Strong Rules 333
  Data Format 335
  The Process of Rule Selection 336
  Interpreting the Results 337
  Rules and Chance 339
  Example 2: Rules for Similar Book Purchases 340
  14.2 Collaborative Filtering 342
  Data Type and Format 343
  Example 3: Netflix Prize Contest 343
  User-Based Collaborative Filtering: "People Like You" 344
  Item-Based Collaborative Filtering 347
  Advantages and Weaknesses of Collaborative Filtering 348
  Collaborative Filtering vs. Association Rules 349
  14.3 Summary 351
  Problems 352
  
  CHAPTER 15 Cluster Analysis 357
  15.1 Introduction 357
  Example: Public Utilities 359
  15.2 Measuring Distance Between Two Records 361
  Euclidean Distance 361
  Normalizing Numerical Measurements 362
  Other Distance Measures for Numerical Data 362
  Distance Measures for Categorical Data 365
  Distance Measures for Mixed Data 366
  15.3 Measuring Distance Between Two Clusters 366
  Minimum Distance 366
  Maximum Distance 366
  Average Distance 367
  Centroid Distance 367
  15.4 Hierarchical (Agglomerative) Clustering 368
  Single Linkage 369
  Complete Linkage 370
  Average Linkage 370
  Centroid Linkage 370
  Ward's Method 370
  Dendrograms: Displaying Clustering Process and Results 371
  Validating Clusters 373
  Limitations of Hierarchical Clustering 375
  15.5 Non-Hierarchical Clustering: The k-Means Algorithm 376
  Choosing the Number of Clusters (k) 377
  Problems 382
  
  PART VI FORECASTING TIME SERIES
  CHAPTER 16 Handling Time Series 387
  16.1 Introduction 387
  16.2 Descriptive vs. Predictive Modeling 389
  16.3 Popular Forecasting Methods in Business 389
  Combining Methods 389
  16.4 Time Series Components 390
  Example: Ridership on Amtrak Trains 390
  16.5 Data-Partitioning and Performance Evaluation 395
  Benchmark Performance: Naive Forecasts 395
  Generating Future Forecasts 396
  Problems 398
  CHAPTER 17 Regression-Based Forecasting 401
  17.1 A Model with Trend 401
  Linear Trend 401
  Exponential Trend 405
  Polynomial Trend 407
  17.2 A Model with Seasonality 407
  17.3 A Model with Trend and Seasonality 411
  17.4 Autocorrelation and ARIMA Models 412
  Computing Autocorrelation 413
  Improving Forecasts by Integrating Autocorrelation Information 416
  Evaluating Predictability 420
  Problems 422
  
  CHAPTER 18 Smoothing Methods 433
  18.1 Introduction 433
  18.2 Moving Average 434
  Centered Moving Average for Visualization 434
  Trailing Moving Average for Forecasting 435
  Choosing Window Width (w) 439
  18.3 Simple Exponential Smoothing 439
  Choosing Smoothing Parameter 440
  Relation Between Moving Average and Simple Exponential Smoothing 440
  18.4 Advanced Exponential Smoothing 442
  Series with a Trend 442
  Series with a Trend and Seasonality 443
  Series with Seasonality (No Trend) 443
  Problems 446
  
  PART VII DATA ANALYTICS
  CHAPTER 19 Social Network Analytics 455
  19.1 Introduction 455
  19.2 Directed vs. Undirected Networks 457
  19.3 Visualizing and Analyzing Networks 458
  Graph Layout 458
  Edge List 460
  Adjacency Matrix 461
  Using Network Data in Classification and Prediction 461
  19.4 Social Data Metrics and Taxonomy 462
  Node-Level Centrality Metrics 463
  Egocentric Network 463
  Network Metrics 465
  19.5 Using Network Metrics in Prediction and Classification 467
  Link Prediction 467
  Entity Resolution 467
  Collaborative Filtering 468
  19.6 Collecting Social Network Data with R 471
  19.7 Advantages and Disadvantages 474
  Problems 476
  
  CHAPTER 20 Text Mining 479
  20.1 Introduction 479
  20.2 The Tabular Representation of Text: Term-Document Matrix and "Bag-of-Words" 480
  20.3 Bag-of-Words vs. Meaning Extraction at Document Level 481
  20.4 Preprocessing the Text 482
  Tokenization 484
  Text Reduction 485
  Presence/Absence vs. Frequency 487
  Term Frequency-Inverse Document Frequency (TF-IDF) 487
  From Terms to Concepts: Latent Semantic Indexing 488
  Extracting Meaning 489
  20.5 Implementing Data Mining Methods 489
  20.6 Example: Online Discussions on Autos and Electronics 490
  Importing and Labeling the Records 490
  Text Preprocessing in R 491
  Producing a Concept Matrix 491
  Fitting a Predictive Model 492
  Prediction 492
  20.7 Summary 494
  Problems 495
  
  PART VIII CASES
  CHAPTER 21 Cases 499
  21.1 Charles Book Club 499
  The Book Industry 499
  Database Marketing at Charles 500
  Data Mining Techniques 502
  Assignment 504
  21.2 German Credit 505
  Background 505
  Data 506
  Assignment 507
  21.3 Tayko Software Cataloger 510
  Background 510
  The Mailing Experiment 510
  Data 510
  Assignment 512
  21.4 Political Persuasion 513
  Background 513
  Predictive Analytics Arrives in US Politics 513
  Political Targeting 514
  Uplift 514
  Data 515
  Assignment 516
  21.5 Taxi Cancellations 517
  Business Situation 517
  Assignment 517
  21.6 Segmenting Consumers of Bath Soap 518
  Business Situation 518
  Key Problems 519
  Data 519
  Measuring Brand Loyalty 519
  Assignment 521
  21.7 Direct-Mail Fundraising 521
  Background 521
  Data 522
  Assignment 523
  21.8 Catalog Cross-Selling 524
  Background 524
  Assignment 524
  21.9 Predicting Bankruptcy 525
  Predicting Corporate Bankruptcy 525
  Assignment 526
  21.10 Time Series Case: Forecasting Public Transportation Demand 528
  Background 528
  Problem Description 528
  Available Data 528
  Assignment Goal 528
  Assignment 529
  Tips and Suggested Steps 529
  
  References 531
  Data Files Used in the Book 533
  Index 535

기본정보

ISBN	9781118879368 ( 1118879368 )
발행(출시)일자	2018년 01월 01일
쪽수	576쪽
크기	184 * 263 * 35 mm / 1192 g
총권수	1권
언어	영어