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

<p>Forward xiii</p>
<p>Preface xv</p>
<p>Acknowledgments xix</p>
<p>Introduction 1</p>
<p>Big Data Timeline 5</p>
<p>Why This Topic Is Relevant Now 8</p>
<p>Is Big Data a Fad? 9</p>
<p>Where Using Big Data Makes a Big Difference 12</p>
<p>Part One The Computing Environment 23</p>
<p>Chapter 1 Hardware 27</p>
<p>Storage (Disk) 27</p>
<p>Central Processing Unit 29</p>
<p>Memory 31</p>
<p>Network 33</p>
<p>Chapter 2 Distributed Systems 35</p>
<p>Database Computing 36</p>
<p>File System Computing 37</p>
<p>Considerations 39</p>
<p>Chapter 3 Analytical Tools 43</p>
<p>Weka 43</p>
<p>Java and JVM Languages 44</p>
<p>R 47</p>
<p>Python 49</p>
<p>SAS 50</p>
<p>Part Two Turning Data into Business Value 53</p>
<p>Chapter 4 Predictive Modeling 55</p>
<p>A Methodology for Building Models 58<br /> <br /> sEMMA 61</p>
<p>Binary Classification 64</p>
<p>Multilevel Classification 66</p>
<p>Interval Prediction 66</p>
<p>Assessment of Predictive Models 67</p>
<p>Chapter 5 Common Predictive Modeling Techniques 71</p>
<p>RFM 72</p>
<p>Regression 75</p>
<p>Generalized Linear Models 84</p>
<p>Neural Networks 90</p>
<p>Decision and Regression Trees 101</p>
<p>Support Vector Machines 107</p>
<p>Bayesian Methods Network Classification 113</p>
<p>Ensemble Methods 124</p>
<p>Chapter 6 Segmentation 127</p>
<p>Cluster Analysis 132</p>
<p>Distance Measures (Metrics) 133</p>
<p>Evaluating Clustering 134</p>
<p>Number of Clusters 135</p>
<p>K?]means Algorithm 137</p>
<p>Hierarchical Clustering 138</p>
<p>Profiling Clusters 138</p>
<p>Chapter 7 Incremental Response Modeling 141</p>
<p>Building the Response Model 142</p>
<p>Measuring the Incremental Response 143</p>
<p>Chapter 8 Time Series Data Mining 149</p>
<p>Reducing Dimensionality 150</p>
<p>Detecting Patterns 151</p>
<p>Time Series Data Mining in Action: Nike+ FuelBand 154</p>
<p>Chapter 9 Recommendation Systems 163</p>
<p>What Are Recommendation Systems? 163</p>
<p>Where Are They Used? 164</p>
<p>How Do They Work? 165</p>
<p>Assessing Recommendation Quality 170</p>
<p>Recommendations in Action: SAS Library 171</p>
<p>Chapter 10 Text Analytics 175</p>
<p>Information Retrieval 176</p>
<p>Content Categorization 177</p>
<p>Text Mining 178</p>
<p>Text Analytics in Action: Let s Play Jeopardy! 180</p>
<p>Part Three Success Stories of Putting It All Together 193</p>
<p>Chapter 11 Case Study of a Large U.S.?]Based Financial Services Company 197</p>
<p>Traditional Marketing Campaign Process 198</p>
<p>High?]Performance Marketing Solution 202</p>
<p>Value Proposition for Change 203</p>
<p>Chapter 12 Case Study of a Major Health Care Provider 205</p>
<p>CAHPS 207</p>
<p>HEDIS 207</p>
<p>HOS 208</p>
<p>IRE 208</p>
<p>Chapter 13 Case Study of a Technology Manufacturer 215</p>
<p>Finding Defective Devices 215</p>
<p>How They Reduced Cost 216</p>
<p>Chapter 14 Case Study of Online Brand Management 221</p>
<p>Chapter 15 Case Study of Mobile Application Recommendations 225</p>
<p>Chapter 16 Case Study of a High?]Tech Product Manufacturer 229</p>
<p>Handling the Missing Data 230</p>
<p>Application beyond Manufacturing 231</p>
<p>Chapter 17 Looking to the Future 233</p>
<p>Reproducible Research 234</p>
<p>Privacy with Public Data Sets 234</p>
<p>The Internet of Things 236</p>
<p>Software Development in the Future 237</p>
<p>Future Development of Algorithms 238</p>
<p>In Conclusion 241</p>
<p>About the Author 243</p>
<p>Appendix 245</p>
<p>References 247</p>
<p>Index 253</p>