Big Data, Data Mining, and Machine Learning Value Creation for Business Leaders and Practitioners

Big data is big business. But having the data and the computational power to process it isn't nearly enough to produce meaningful results. Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners is a complete resource for technology and marketing executives looking to cut through the hype and produce real results that hit the bottom line. Providing an engaging, thorough overview of the current state of big data analytics and the growing trend toward high performance computing architectures, the book is a detail-driven look into how big data analytics can be leveraged to foster positive change and drive efficiency.

With continued exponential growth in data and ever more competitive markets, businesses must adapt quickly to gain every competitive advantage available. Big data analytics can serve as the linchpin for initiatives that drive business, but only if the underlying technology and analysis is fully understood and appreciated by engaged stakeholders. This book provides a view into the topic that executives, managers, and practitioners require, and includes:

• A complete overview of big data and its notable characteristics
• Details on high performance computing architectures for analytics, massively parallel processing (MPP), and in-memory databases
• Comprehensive coverage of data mining, text analytics, and machine learning algorithms
• A discussion of explanatory and predictive modeling, and how they can be applied to decision-making processes

Big Data, Data Mining, and Machine Learning provides technology and marketing executives with the complete resource that has been notably absent from the veritable libraries of published books on the topic. Take control of your organization's big data analytics to produce real results with a resource that is comprehensive in scope and light on hyperbole.

Jared Dean (Cary, NC) is Director of Research and Development at SAS Institute. He is responsible for all aspects of development in SAS' global 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.  He holds a MS degree in computational statistics from George Mason University.

Khosrow Hassibi (San Francisco) is a Principal, Advanced Analytics Solutions, at SAS Institute. Prior to SAS, he has spent most of his career in data mining software start-ups such as HNC Software and KXEN. He has world-class expertise in design/development/deployment of the state-of-the-art data mining and predictive analytics solutions in fraud, marketing, and risk. Hassibi has a Ph.D. in EECS from Case Western Reserve University with a special concentration in the areas of intelligent systems and machine learning and a certificate in “The Executive Program in Management” from UCLA Anderson Graduate School of Management.

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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