Dean Big Data, Data Mining, and Machine Learning

Value Creation for Business Leaders and Practitioners
1. Auflage 2014
ISBN: 978-1-118-92069-5
Verlag: John Wiley & Sons
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 288 Seiten, E-Book

Reihe: SAS Institute Inc

With big data analytics comes big insights intoprofitability
Big data is big business. But having the data and thecomputational power to process it isn't nearly enough to producemeaningful results. Big Data, Data Mining, and Machine Learning:Value Creation for Business Leaders and Practitioners is acomplete resource for technology and marketing executives lookingto cut through the hype and produce real results that hit thebottom line. Providing an engaging, thorough overview of thecurrent state of big data analytics and the growing trend towardhigh performance computing architectures, the book is adetail-driven look into how big data analytics can be leveraged tofoster positive change and drive efficiency.
With continued exponential growth in data and ever morecompetitive markets, businesses must adapt quickly to gain everycompetitive advantage available. Big data analytics can serve asthe linchpin for initiatives that drive business, but only if theunderlying technology and analysis is fully understood andappreciated by engaged stakeholders. This book provides a view intothe topic that executives, managers, and practitioners require, andincludes:
* A complete overview of big data and its notablecharacteristics
* Details on high performance computing architectures foranalytics, massively parallel processing (MPP), and in-memorydatabases
* Comprehensive coverage of data mining, text analytics, andmachine learning algorithms
* A discussion of explanatory and predictive modeling, and howthey can be applied to decision-making processes
Big Data, Data Mining, and Machine Learning providestechnology and marketing executives with the complete resource thathas been notably absent from the veritable libraries of publishedbooks on the topic. Take control of your organization's big dataanalytics to produce real results with a resource that iscomprehensive in scope and light on hyperbole.

Autoren/Hrsg.

Dean, Jared

Inhaltsverzeichnis

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 Together193
Chapter 11 Case Study of a Large U.S.?]Based FinancialServices 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 Provider205
CAHPS 207
HEDIS 207
HOS 208
IRE 208
Chapter 13 Case Study of a Technology Manufacturer215
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 Recommendations225
Chapter 16 Case Study of a High?]Tech Product Manufacturer229
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

Über Autor(innen)

JARED DEAN is a Senior Director of Research andDevelopment at SAS Institute. He is responsible for the developmentof SAS's worldwide data mining solutions. This includescustomer engagements, new feature development, technical support,sales support, and product integration. Prior to joining SAS, Deanworked as a Mathematical Statistician for the US Census Bureau.