I. History of Phases of Data Analysis,
Basic Theory, and Data Mining Process
1. The Background for Data Mining Practice
2. Theoretical Considerations for Data Mining
3. The Data Mining Process
4. Data Understanding and Preparation
5. Feature Selection
6. Accessory Tools for Doing Data Mining
II. The Algorithms in Data Mining and Text Mining,
The Organization of the Three Most Common Data
Mining Tools, and Selected Specialized Areas Using
Data Mining
7. Basic Algorithms for Data Mining: A Brief Overview
8. Advanced Algorithms for Data Mining
9. Text Mining and Natural Language Processing
10. The Three Most Common Data Mining Software Tools
11. Classification
12. Numerical Prediction
13. Model Evaluation and Enhancement
14. Medical Informatics
15. Bioinformatics
16. Customer Response Modeling
17. Fraud Detection
III. Tutorials - Step-by-Step Case Studies as A Staring
Point to Learn How to Do Data Mining Analyses
A. How to Use Data Miner Recipe
B. Data Mining for Aviation Safety
C. Predicting Movie Box-Office Receipts
D. Detecting Unsatisfied Customers: A Case Study
E. Credit Scoring
F. Churn Analysis
G. Text Mining: Automobile Brand Review
H. Predictive Process Control: QC-Data Mining
I. Business Administration in a Medical Industry
J. Clinical Psychology: Making Decision About
Best Therapy for a Client
K. Education-Leadership Training for Business
and Education
L. Dentistry: Facial Pain Study
M. Profit Analysis of the German Credit Data
N. Predicting Self-Reported Health Status Using
Artificial Neural Networks
IV. Measuring True Complexity, The "Right Model
for the Right Use", Top Mistakes, and the Future
of Analytics
18. Model Complexity (and How Ensembles Help)
19. The Right Model for the Right Purpose:
When Less is Good Enough
20. Top 10 Data Mining Mistakes
21. Prospects for the Future of Data Mining
and Text Mining as Part of Our Everyday Lives
22. Summary: Our Design
