Handbook of statistical analysis and data mining applications/
Robert Nisbet
- Elsevier, 2018.
- 822 p.
Chapter 1 - The Background for Data Mining Practice
Chapter 2 - Theoretical Considerations for Data Mining
Chapter 3 - The Data Mining and Predictive Analytic Process
Chapter 4 - Data Understanding and Preparation
Chapter 5 - Feature Selection
Chapter 6 - Accessory Tools for Doing Data Mining
Chapter 7 - Basic Algorithms for Data Mining: A Brief Overview
Chapter 8 - Advanced Algorithms for Data Mining
Chapter 9 - Classification
Chapter 10 - Numerical Prediction
Chapter 11 - Model Evaluation and Enhancement
Chapter 12 - Predictive Analytics for Population Health and Care
Chapter 13 - Big Data in Education: New Efficiencies for Recruitment, Learning, and Retention of Students and Donors
Chapter 14 - Customer Response Modeling
Chapter 15 - Fraud Detection
Chapter 16 - The Apparent Paradox of Complexity in Ensemble Modeling
Chapter 17 - The “Right Model” for the “Right Purpose”: When Less Is Good Enough
Chapter 18 - A Data Preparation Cookbook
Chapter 19 - Deep Learning
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”