Files and data associated with the tutorials may be found here.
Preface
Forwards (Dean Abbott and Tony Lachenbruch)
Introduction
PART I: History of Phases of Data Analysis, Basic Theory, and the Data Mining Process
Chapter 1. History - The Phases of Data Analysis throughout the Ages
Chapter 2. Theory
Chapter 3. The Data Mining Process
Chapter 4. Data Understanding and Preparation
Chapter 5. Feature Selection - Selecting the Best Variables
Chapter 6: Accessory Tools and Advanced Features in Data
PART II: - The Algorithms in Data Mining and Text Mining, and the Organization of the Three most common Data Mining Tools
Chapter 7. Basic Algorithms
Chapter 8: Advanced Algorithms
Chapter 9. Text Mining
Chapter 10. Organization of 3 Leading Data Mining Tools
Chapter 11. Classification Trees = Decision Trees
Chapter 12. Numerical Prediction (Neural Nets and GLM)
Chapter 13. Model Evaluation and Enhancement
Chapter 14. Medical Informatics
Chapter 15. Bioinformatics
Chapter 16. Customer Response Models
Chapter 17. Fraud Detection
PART III: Tutorials - Step-by-Step Case Studies as a Starting Point to learn how to do Data Mining Analyses
Listing of Guest Authors of the Tutorials
Tutorials within the book pages:
How to use the DMRecipe
Aviation Safety using DMRecipe
Movie Box-Office Hit Prediction using SPSS CLEMENTINE
Bank Financial data - using SAS-EM
Credit Scoring
CRM Retention using CLEMENTINE
Automobile - Cars - Text Mining
Quality Control using Data Mining
Three integrated tutorials from different domains, but all using C&RT to predict and display possible structural relationships among data:
Business Administration in a Medical Industry
Clinical Psychology- Finding Predictors of Correct Diagnosis
Education - Leadership Training: for Business and Education
Additional tutorials are available either on the accompanying CD-DVD, or the Elsevier Web site for this book
Listing of Tutorials on Accompanying CD
PART IV: Paradox of Complex Models; using the “right model for the right use”, on-going development, and the Future.
Chapter 18: Paradox of Ensembles and Complexity
Chapter 19: The Right Model for the Right Use
Chapter 20: The Top 10 Data Mining Mistakes
Chapter 21: Prospect for the Future - Developing Areas in Data Mining
Chapter 22: Summary
GLOSSARY of STATISICAL and DATA MINING TERMS
INDEX
CD - With Additional Tutorials, data sets, Power Points, and Data Mining software (STATISTICA Data Miner & Text Miner & QC-Miner - 90 day free trial)