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Data Mining for Business Analytics: Concepts, Techniques, and Applications with XLMiner, 3rd Edition

ISBN: 978-1-118-72927-4
552 pages
April 2016, ©2016
Data Mining for Business Analytics: Concepts, Techniques, and Applications with XLMiner, 3rd Edition (1118729277) cover image

Description

Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner®, Third Edition presents an applied approach to data mining and predictive analytics with clear exposition, hands-on exercises, and real-life case studies. Readers will work with all of the standard data mining methods using the Microsoft® Office Excel® add-in XLMiner® to develop predictive models and learn how to obtain business value from Big Data.

Featuring updated topical coverage on text mining, social network analysis, collaborative filtering, ensemble methods, uplift modeling and more, the Third Edition also includes:

  • Real-world examples to build a theoretical and practical understanding of key data mining methods 
  • End-of-chapter exercises that help readers better understand the presented material
  • Data-rich case studies to illustrate various applications of data mining techniques
  • Completely new chapters on social network analysis and text mining
  • A companion site with additional data sets, instructors material that include solutions to exercises and case studies, and Microsoft PowerPoint® slides
  • Free 140-day license to use XLMiner for Education software

Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner®, Third Edition is an ideal textbook for upper-undergraduate and graduate-level courses as well as professional programs on data mining, predictive modeling, and Big Data analytics. The new edition is also a unique reference for analysts, researchers, and practitioners working with predictive analytics in the fields of business, finance, marketing, computer science, and information technology.

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. have done a wonderful job in presenting the field of data mining - a welcome addition to the literature." – ComputingReviews.com

"Excellent choice for business analysts...The book is a perfect fit for its intended audience."  – Keith McCormick, Consultant and Author of SPSS Statistics For Dummies, Third Edition and SPSS Statistics for Data Analysis and Visualization

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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Table of Contents

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

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Author Information

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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