With big data analytics comes big insights into profitability 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.
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 Classifi cation 64
Multilevel Classifi cation 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 Classifi cation 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
Profi ling 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
