Data Mining for Business Analytics: Concepts, Techniques, and Applications in R

Data Mining for Business Analytics: Concepts, Techniques, and Applications in R presents an applied approach to data mining concepts and methods, using R software for illustration Readers will learn how to implement a variety of popular data mining algorithms in R (a free and open–source software) to tackle business problems and opportunities. This is the fifth version of this successful text, and the first using R. It covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, recommender systems, clustering, text mining and network analysis. It also includes: Two new co–authors, Inbal Yahav and Casey Lichtendahl, who bring both expertise teaching business analytics courses using R, and data mining consulting experience in business and government Updates and new material based on feedback from instructors teaching MBA, undergraduate, diploma and executive courses, and from their students More than a dozen case studies demonstrating applications for the data mining techniques described End–of–chapter exercises that help readers gauge and expand their comprehension and competency of the material presented A companion website with more than two dozen data sets, and instructor materials including exercise solutions, PowerPoint slides, and case solutions Data Mining for Business Analytics: Concepts, Techniques, and Applications in R is an ideal textbook for graduate and upper–undergraduate level courses in data mining, predictive analytics, and business analytics. This new edition is also an excellent reference for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology. This book has by far the most comprehensive review of business analytics methods that I have ever seen, covering everything from classical approaches such as linear and logistic regression, through to modern methods like neural networks, bagging and boosting, and even much more business specific procedures such as social network analysis and text mining. If not the bible, it is at the least a definitive manual on the subject. Gareth M. James,  University of Southern California and co–author (with Witten, Hastie and Tibshirani) of the best–selling book An Introduction to Statistical Learning, with Applications in R 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, 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 publications including books. Peter C. Bruce is President and Founder of the Institute for Statistics Education at 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 (Wiley) and co–author of Practical Statistics for Data Scientists: 50 Essential Concepts (O Reilly). Inbal Yahav, PhD, is Professor at the Graduate School of Business Administration at Bar–Ilan University, Israel. She teaches courses in social network analysis, advanced research methods, and software quality assurance. Dr. Yahav received her PhD in Operations Research and Data Mining from the University of Maryland, College Park. 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. Kenneth C. Lichtendahl, Jr., PhD, is Associate Professor at the University of Virginia. He is the Eleanor F. and Phillip G. Rust Professor of Business Administration and teaches MBA courses in decision analysis, data analysis and optimization, and managerial quantitative analysis. He also teaches executive education courses in strategic analysis and decision–making, and managing the corporate aviation function. INDICE: Contents .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

• ISBN: 978-1-118-87936-8
• Editorial: Wiley–Blackwell
• Encuadernacion: Cartoné
• Páginas: 576
• Fecha Publicación: 06/10/2017
• Nº Volúmenes: 1
• Idioma: Inglés