ISBN-10:
1118618041
ISBN-13:
9781118618042
Pub. Date:
05/27/2014
Publisher:
Wiley
Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners / Edition 1

Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners / Edition 1

by Jared Dean
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Product Details

ISBN-13: 9781118618042
Publisher: Wiley
Publication date: 05/27/2014
Series: Wiley and SAS Business Series
Pages: 288
Sales rank: 837,477
Product dimensions: 6.20(w) x 9.10(h) x 1.10(d)

About the Author

JARED DEAN is a Senior Director of Research and Development at SAS Institute. He is responsible for the development of SAS’s worldwide data mining solutions. This includes customer engagements, new feature development, technical support, sales support, and product integration. Prior to joining SAS, Dean worked as a Mathematical Statistician for the US Census Bureau.

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Table of Contents

Forward xiii

Preface xv

Acknowledgments xix

Introduction 1

Big Data Timeline 5

Why This Topic Is Relevant Now 8

Is Big Data a Fad? 9

Where Using Big Data Makes a Big Difference 12

Part One The Computing Environment 23

Chapter 1 Hardware 27

Storage (Disk) 27

Central Processing Unit 29

Memory 31

Network 33

Chapter 2 Distributed Systems 35

Database Computing 36

File System Computing 37

Considerations 39

Chapter 3 Analytical Tools 43

Weka 43

Java and JVM Languages 44

R 47

Python 49

SAS 50

Part Two Turning Data into Business Value 53

Chapter 4 Predictive Modeling 55

A Methodology for Building Models 58

sEMMA 61

Binary Classification 64

Multilevel Classification 66

Interval Prediction 66

Assessment of Predictive Models 67

Chapter 5 Common Predictive Modeling Techniques 71

RFM 72

Regression 75

Generalized Linear Models 84

Neural Networks 90

Decision and Regression Trees 101

Support Vector Machines 107

Bayesian Methods Network Classification 113

Ensemble Methods 124

Chapter 6 Segmentation 127

Cluster Analysis 132

Distance Measures (Metrics) 133

Evaluating Clustering 134

Number of Clusters 135

K-means Algorithm 137

Hierarchical Clustering 138

Profiling Clusters 138

Chapter 7 Incremental Response Modeling 141

Building the Response Model 142

Measuring the Incremental Response 143

Chapter 8 Time Series Data Mining 149

Reducing Dimensionality 150

Detecting Patterns 151

Time Series Data Mining in Action: Nike+ FuelBand 154

Chapter 9 Recommendation Systems 163

What Are Recommendation Systems? 163

Where Are They Used? 164

How Do They Work? 165

Assessing Recommendation Quality 170

Recommendations in Action: SAS Library 171

Chapter 10 Text Analytics 175

Information Retrieval 176

Content Categorization 177

Text Mining 178

Text Analytics in Action: Let’s Play Jeopardy! 180

Part Three Success Stories of Putting It All Together 193

Chapter 11 Case Study of a Large U.S.-Based Financial Services Company 197

Traditional Marketing Campaign Process 198

High-Performance Marketing Solution 202

Value Proposition for Change 203

Chapter 12 Case Study of a Major Health Care Provider 205

CAHPS 207

HEDIS 207

HOS 208

IRE 208

Chapter 13 Case Study of a Technology Manufacturer 215

Finding Defective Devices 215

How They Reduced Cost 216

Chapter 14 Case Study of Online Brand Management 221

Chapter 15 Case Study of Mobile Application Recommendations 225

Chapter 16 Case Study of a High-Tech Product Manufacturer 229

Handling the Missing Data 230

Application beyond Manufacturing 231

Chapter 17 Looking to the Future 233

Reproducible Research 234

Privacy with Public Data Sets 234

The Internet of Things 236

Software Development in the Future 237

Future Development of Algorithms 238

In Conclusion 241

About the Author 243

Appendix 245

References 247

Index 253

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