Table of Contents

What Is Data Mining and What Can It Do?
Introduction
A Brief Philosophical Discussion
The Most Important Attribute of the Successful Data Miner: Integrity
What Does Data Mining Do?
What Do We Mean By Data?
Data Complexity
Computational Complexity
Summary

The Data Mining Process
Introduction
Discovery and Exploitation
Eleven Key Principles of Information Driven Data Mining
Key Principles Expanded
Type of Models: Descriptive, Predictive, Forensic
Data Mining Methodologies
A Generic Data Mining Process
RAD Skill Set Designators
Summary

Problem Definition (Step 1)
Introduction
Problem Definition Task 1: Characterize Your Problem
Problem Definition Checklist
Candidate Solution Checklist
Problem Definition Task 2: Characterizing Your Solution
Problem Definition Case Study
Summary

Data Evaluation (Step 2)
Introduction
Data Accessibility Checklist
How Much Data Do You Need?
Data Staging
Methods Used for Data Evaluation
Data Evaluation Case Study: Estimating the Information Content Features
Some Simple Data Evaluation Methods
Data Quality Checklist
Summary

Feature Extraction and Enhancement (Step 3)
Introduction: A Quick Tutorial on Feature Space
Characterizing and Resolving Data Problems
Principal Component Analysis
Synthesis of Features
Degapping
Summary

Prototyping Plan and Model Development (Step 4)
Introduction
Step 4A: Prototyping Plan
Prototyping Plan Case Study
Step 4B: Prototyping/Model Development
Model Development Case Study
Summary

Model Evaluation (Step 5)
Introduction
Evaluation Goals and Methods
What Does Accuracy Mean?
Summary

Implementation (Step 6)
Introduction
Quantifying the Benefits of Data Mining
Tutorial on Ensemble Methods
Getting It Wrong: Mistakes Every Data Miner Has Made
Summary

Supervised Learning Genre Section 1—Detecting and Characterizing Known Patterns
Introduction
Representative Example of Supervised Learning: Building a Classifier
Specific Challenges, Problems, and Pitfalls of Supervised Learning
Recommended Data Mining Architectures for Supervised Learning
Descriptive Analysis
Predictive Modeling
Summary

Forensic Analysis Genre Section 2—Detecting, Characterizing, and Exploiting Hidden Patterns
Introduction
Genre Overview
Recommended Data Mining Architectures for Unsupervised Learning
Examples and Case Studies for Unsupervised Learning
Tutorial on Neural Networks
Making Syntactic Methods Smarter: The Search Engine Problem
Summary

Genre Section 3—Knowledge: Its Acquisition, Representation, and Use
Introduction to Knowledge Engineering
Computing with Knowledge
Inferring Knowledge from Data: Machine Learning
Summary

References
Glossary
Index