Handbook of Statistical Analysis and Data Mining Applications, Second Edition, is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers, both academic and industrial, through all stages of data analysis, model building and implementation. The handbook helps users discern technical and business problems, understand the strengths and weaknesses of modern data mining algorithms and employ the right statistical methods for practical application.
This book is an ideal reference for users who want to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques and discusses their application to real problems in ways accessible and beneficial to practitioners across several areas—from science and engineering, to medicine, academia and commerce.
1. Introduction - p. xxi 2. Frontispiece - p. xxv 3. Biographies of the Primary Authors of This Book - p. xxvii 4. Part I: History of Phases of Data Analysis, Basic Theory, and the Data Mining Process - p. xxix 5. Chapter 1: The Background for Data Mining Practice - p. xxix 6. Chapter 2: Theoretical Considerations for Data Mining - p. 20 7. Chapter 3: The Data Mining and Predictive Analytic Process - p. 37 8. Chapter 4: Data Understanding and Preparation - p. 54 9. Chapter 5: Feature Selection - p. 82 10. Chapter 6: Accessory Tools for Doing Data Mining - p. 97 11. Part II: The Algorithms and Methods in Data Mining and Predictive Analytics and Some Domain Areas - p. 117 12. Introduction - p. 119 13. Chapter 7: Basic Algorithms for Data Mining: A Brief Overview - p. 120 14. Chapter 8: Advanced Algorithms for Data Mining - p. 147 15. Chapter 9: Classification - p. 167 16. Chapter 10: Numerical Prediction - p. 186 17. Chapter 11: Model Evaluation and Enhancement - p. 213 18. Chapter 12: Predictive Analytics for Population Health and Care - p. 233 19. Chapter 13: Big Data in Education: New Efficiencies for Recruitment, Learning, and Retention of Students and Donors - p. 258 20. Chapter 14: Customer Response Modeling - p. 277 21. Chapter 15: Fraud Detection - p. 288 22. Part III: Tutorials and Case Studies - p. 302 23. Introduction - p. 303 24. Tutorial A: Example of Data Mining Recipes Using Windows 10 and Statistica 13 - p. 304 25. Tutorial B: Using the Statistica Data Mining Workspace Method for Analysis of Hurricane Data (Hurrdata.sta) - p. 317 26. Tutorial C: Case Study—Using SPSS Modeler and STATISTICA to Predict Student Success at High-Stakes Nursing Examinations (NCLEX) - p. 333 27. Tutorial D: Constructing a Histogram in KNIME Using MidWest Company Personality Data - p. 357 28. Tutorial E: Feature Selection in KNIME - p. 376 29. Tutorial F: Medical/Business Tutorial - p. 391 30. Tutorial G: A KNIME Exercise, Using Alzheimer's Training Data of Tutorial F - p. 422 31. Tutorial H: Data Prep 1-1: Merging Data Sources - p. 442 32. Tutorial I: Data Prep 1–2: Data Description - p. 458 33. Tutorial J: Data Prep 2-1: Data Cleaning and Recoding - p. 469 34. Tutorial K: Data Prep 2-2: Dummy Coding Category Variables - p. 496 35. Tutorial L: Data Prep 2-3: Outlier Handling - p. 514 36. Tutorial M: Data Prep 3-1: Filling Missing Values With Constants - p. 528 37. Tutorial N: Data Prep 3-2: Filling Missing Values With Formulas - p. 538 38. Tutorial O: Data Prep 3-3: Filling Missing Values With a Model - p. 564 39. Tutorial P: City of Chicago Crime Map: A Case Study Predicting Certain Kinds of Crime Using Statistica Data Miner and Text Miner - p. 596 40. Tutorial Q: Using Customer Churn Data to Develop and Select a Best Predictive Model for Client Defection Using STATISTICA Data Miner 13 64-bit for Windows 10 - p. 625 41. Tutorial R: Example With C&RT to Predict and Display Possible Structural Relationships, - p. 650 42. Tutorial S: Clinical Psychology: Making Decisions About Best Therapy for a Client - p. 674 43. Part IV: Model Ensembles, Model Complexity; Using the Right Model for the Right Use, Significance, Ethics, and The Future, and Advanced Processes - p. 701 44. Introduction - p. 703 45. Chapter 16: The Apparent Paradox of Complexity in Ensemble Modeling - p. 703 46. Chapter 17: The “Right Model” for the “Right Purpose”: When Less Is Good Enough - p. 718 47. Chapter 18: A Data Preparation Cookbook - p. 726 48. Chapter 19: Deep Learning - p. 740 49. Chapter 20: Significance versus Luck in the Age of Mining: The Issues of P-Value “Significance” and “Ways to Test Significance of Our Predictive Analytic Models” - p. 751 50. Chapter 21: Ethics and Data Analytics - p. 765 51. Chapter 22: IBM Watson - p. 772 52. Index - p. 783
Handbook of Statistical Analysis and Data Mining Applications, Second Edition, is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers, both academic and industrial, through all stages of data analysis, model building and implementation. The handbook helps users discern technical and business problems, understand the strengths and weaknesses of modern data mining algorithms and employ the right statistical methods for practical application.
This book is an ideal reference for users who want to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques and discusses their application to real problems in ways accessible and beneficial to practitioners across several areas—from science and engineering, to medicine, academia and commerce.
1. Introduction - p. xxi 2. Frontispiece - p. xxv 3. Biographies of the Primary Authors of This Book - p. xxvii 4. Part I: History of Phases of Data Analysis, Basic Theory, and the Data Mining Process - p. xxix 5. Chapter 1: The Background for Data Mining Practice - p. xxix 6. Chapter 2: Theoretical Considerations for Data Mining - p. 20 7. Chapter 3: The Data Mining and Predictive Analytic Process - p. 37 8. Chapter 4: Data Understanding and Preparation - p. 54 9. Chapter 5: Feature Selection - p. 82 10. Chapter 6: Accessory Tools for Doing Data Mining - p. 97 11. Part II: The Algorithms and Methods in Data Mining and Predictive Analytics and Some Domain Areas - p. 117 12. Introduction - p. 119 13. Chapter 7: Basic Algorithms for Data Mining: A Brief Overview - p. 120 14. Chapter 8: Advanced Algorithms for Data Mining - p. 147 15. Chapter 9: Classification - p. 167 16. Chapter 10: Numerical Prediction - p. 186 17. Chapter 11: Model Evaluation and Enhancement - p. 213 18. Chapter 12: Predictive Analytics for Population Health and Care - p. 233 19. Chapter 13: Big Data in Education: New Efficiencies for Recruitment, Learning, and Retention of Students and Donors - p. 258 20. Chapter 14: Customer Response Modeling - p. 277 21. Chapter 15: Fraud Detection - p. 288 22. Part III: Tutorials and Case Studies - p. 302 23. Introduction - p. 303 24. Tutorial A: Example of Data Mining Recipes Using Windows 10 and Statistica 13 - p. 304 25. Tutorial B: Using the Statistica Data Mining Workspace Method for Analysis of Hurricane Data (Hurrdata.sta) - p. 317 26. Tutorial C: Case Study—Using SPSS Modeler and STATISTICA to Predict Student Success at High-Stakes Nursing Examinations (NCLEX) - p. 333 27. Tutorial D: Constructing a Histogram in KNIME Using MidWest Company Personality Data - p. 357 28. Tutorial E: Feature Selection in KNIME - p. 376 29. Tutorial F: Medical/Business Tutorial - p. 391 30. Tutorial G: A KNIME Exercise, Using Alzheimer's Training Data of Tutorial F - p. 422 31. Tutorial H: Data Prep 1-1: Merging Data Sources - p. 442 32. Tutorial I: Data Prep 1–2: Data Description - p. 458 33. Tutorial J: Data Prep 2-1: Data Cleaning and Recoding - p. 469 34. Tutorial K: Data Prep 2-2: Dummy Coding Category Variables - p. 496 35. Tutorial L: Data Prep 2-3: Outlier Handling - p. 514 36. Tutorial M: Data Prep 3-1: Filling Missing Values With Constants - p. 528 37. Tutorial N: Data Prep 3-2: Filling Missing Values With Formulas - p. 538 38. Tutorial O: Data Prep 3-3: Filling Missing Values With a Model - p. 564 39. Tutorial P: City of Chicago Crime Map: A Case Study Predicting Certain Kinds of Crime Using Statistica Data Miner and Text Miner - p. 596 40. Tutorial Q: Using Customer Churn Data to Develop and Select a Best Predictive Model for Client Defection Using STATISTICA Data Miner 13 64-bit for Windows 10 - p. 625 41. Tutorial R: Example With C&RT to Predict and Display Possible Structural Relationships, - p. 650 42. Tutorial S: Clinical Psychology: Making Decisions About Best Therapy for a Client - p. 674 43. Part IV: Model Ensembles, Model Complexity; Using the Right Model for the Right Use, Significance, Ethics, and The Future, and Advanced Processes - p. 701 44. Introduction - p. 703 45. Chapter 16: The Apparent Paradox of Complexity in Ensemble Modeling - p. 703 46. Chapter 17: The “Right Model” for the “Right Purpose”: When Less Is Good Enough - p. 718 47. Chapter 18: A Data Preparation Cookbook - p. 726 48. Chapter 19: Deep Learning - p. 740 49. Chapter 20: Significance versus Luck in the Age of Mining: The Issues of P-Value “Significance” and “Ways to Test Significance of Our Predictive Analytic Models” - p. 751 50. Chapter 21: Ethics and Data Analytics - p. 765 51. Chapter 22: IBM Watson - p. 772 52. Index - p. 783
