Big Data, Data Mining, and Machine Learning -- Contents -- Forward -- Preface -- Acknowledgments -- Introduction -- Big Data Timeline -- Why This Topic Is Relevant Now -- Is Big Data a Fad? -- Where Using Big Data Makes a Big Difference -- Technical Issue -- Work Flow Productivity -- The Complexities When Data Gets Large -- Part One The Computing Environment -- Chapter 1 Hardware -- Storage (Disk) -- Central Processing Unit -- Graphical Processing Unit -- Memory -- Network -- Chapter 2 Distributed Systems -- Database Computing -- File System Computing -- Considerations -- Chapter 3 Analytical Tools -- Weka -- Java and JVM Languages -- R -- Python -- SAS -- Part Two Turning Data into Business Value -- Chapter 4 Predictive Modeling -- A Methodology for Building Models -- sEMMA -- sEMMA for the Big Data Era -- Binary Classification -- Multilevel Classification -- Interval Prediction -- Assessment of Predictive Models -- Classification -- Receiver Operating Characteristic -- Lift -- Gain -- Akaike's Information Criterion -- Bayesian Information Criterion -- Kolmogorov‐Smirnov -- Chapter 5 Common Predictive Modeling Techniques -- RFM -- Regression -- Basic Example of Ordinary Least Squares -- Assumptions of Regression Models -- Additional Regression Techniques -- Applications in the Big Data Era -- Generalized Linear Models -- Example of a Probit GLM -- Applications in the Big Data Era -- Neural Networks -- Basic Example of Neural Networks -- Decision and Regression Trees -- Support Vector Machines -- Bayesian Methods Network Classification -- Naive Bayes Network -- Parameter Learning -- Learning a Bayesian Network -- Inference in Bayesian Networks -- Scoring for Supervised Learning -- Ensemble Methods -- Chapter 6 Segmentation -- Cluster Analysis -- Distance Measures (Metrics) -- Evaluating Clustering -- Number of Clusters -- K-means Algorithm.

Hierarchical Clustering -- Profiling Clusters -- Chapter 7 Incremental Response Modeling -- Building the Response Model -- Measuring the Incremental Response -- Chapter 8 Time Series Data Mining -- Reducing Dimensionality -- Detecting Patterns -- Fraud Detection -- New Product Forecasting -- Time Series Data Mining in Action: Nike+ FuelBand -- Seasonal Analysis -- Trend Analysis -- Similarity Analysis -- Chapter 9 Recommendation Systems -- What Are Recommendation Systems? -- Where Are They Used? -- How Do They Work? -- Baseline Model -- Low‐Rank Matrix Factorization -- Stochastic Gradient Descent -- Alternating Least Squares -- Restricted Boltzmann Machines -- Contrastive Divergence -- Assessing Recommendation Quality -- Recommendations in Action: SAS Library -- Chapter 10 Text Analytics -- Information Retrieval -- Content Categorization -- Text Mining -- Text Analytics in Action: Let's Play Jeopardy! -- Information Retrieval Steps -- Discovering Topics in Jeopardy! Clues -- Topics from Clues Having Incorrect or Missing Answers -- Discovering New Topics from Clues -- Contestant Analysis: Fantasy Jeopardy! -- Part Three Success Stories of Putting It All Together -- Chapter 11 Case Study of a Large U.S.-Based Financial Services Company -- Traditional Marketing Campaign Process -- High-Performance Marketing Solution -- Value Proposition for Change -- Chapter 12 Case Study of a Major Health Care Provider -- CAHPS -- HEDIS -- HOS -- IRE -- Chapter 13 Case Study of a Technology Manufacturer -- Finding Defective Devices -- How They Reduced Cost -- Chapter 14 Case Study of Online Brand Management -- Chapter 15 Case Study of Mobile Application Recommendations -- Chapter 16 Case Study of a High-Tech Product Manufacturer -- Handling the Missing Data -- Application beyond Manufacturing -- Chapter 17 Looking to the Future -- Reproducible Research.

Privacy with Public Data Sets -- The Internet of Things -- Software Development in the Future -- Future Development of Algorithms -- In Conclusion -- About the Author -- Appendix -- References -- Index.