1.1 Data Mining: A Definition
1.2 What Can Computers Learn?
1.3 Is Data Mining Appropriate for my Problem?
1.4 Expert Systems or Data Mining?
1.5 A Simple Data Mining Process Model
1.6 Why Not Simple Search?
1.7 Data Mining Applications
1.8 Chapter Summary
1.9 Key Terms
1.10 Exercises

2.1 Data Mining Strategies
2.2 Supervised Data Mining Techniques
2.3 Association Rules
2.4 Clustering Techniques
2.5 Evaluating Performance
2.6 Chapter Summary
2.7 Key Terms
2.8 Exercises

3.1 Decision Trees
3.2 Generating Association Rules
3.3 The K-Means Algorithm
3.4 Genetic Learning
3.5 Choosing a Data Mining Technique
3.6 Chapter Summary
3.7 Key Terms
3.8 Exercises

4.1 The iData Analyzer
4.2 ESX: A Multipurpose Tool for Data Mining
4.3 iDAV Format for Data Mining
4.4 A Five-Step Approach for Unsupervised Clustering
4.5 A Six-Step Approach for Supervised Learning
4.6 Techniques for Generating Rules
4.7 Instance Typicality
4.8 Special Considerations and Features
4.9 Chapter Summary
4.10 Key Terms
4.11 Exercises

5.1 A KDD Process Model
5.2 Step 1: Goal Identification
5.3 Step 2: Creating a Target Data Set
5.4 Step 3: Data Preprocessing
5.5 Step 4: Data Transformation
5.6 Step 5: Data Mining
5.7 Step 6: Interpretation and Evaluation
5.8 Step 7: Taking Action
5.9 The CRISP-DM Process Model
5.10 Experimenting with ESX
5.11 Chapter Summary
5.12 Key Terms
5.13 Exercises

6.1 Operational Databases
6.2 Data Warehouse Design
6.3 On-line Analytical Processing (OLAP)
6.4 Excel Pivot Tables for Data Analysis
6.5 Chapter Summary
6.6 Key Terms
6.7 Exercises

7.1 What Should be Evaluated?
7.2 Tools for Evaluation
7.3 Computing Test Set Confidence Intervals
7.4 Comparing Supervised Learner Models
7.5 Attribute Evaluation
7.6 Unsupervised Evaluation Techniques
7.7 Evaluating Supervised Models with Numeric Output
7.8 Chapter Summary
7.9 Key Terms
7.10 Exercises

8.1 Feed-Forward Neural Networks
8.2 Neural Network Training: A Conceptual View
8.3 Neural Network Explanation
8.4 General Considerations
8.5 Neural Network Training: A Detailed View
8.6 Chapter Summary
8.7 Key Terms
8.8 Exercises

9.1 A Four-Step Approach for Backpropagation Learning
9.2 A Four-Step Approach for Neural Network Clustering
9.3 ESX for Neural Network Cluster Analysis
9.4 Chapter Summary
9.5 Key Terms
9.6 Exercises

10.1 Linear Regression Analysis
10.2 Logistic Regression
10.3 Bayes Classifier
10.4 Clustering Algorithms
10.5 Heuristics or Statistics?
10.6 Chapter Summary
10.7 Key Terms
10.8 Exercises

11.1 Time-Series Analysis
11.2 Mining the Web
11.3 Mining Textual Data
11.4 Improving Performance
11.5 Chapter Summary
11.6 Key Terms
11.7 Exercises

12.1 Exploring Artificial Intelligence
12.2 Problem Solving as a State Space Search
12.3 Expert Systems
12.4 Structuring a Rule-Based System
12.5 Chapter Summary
12.6 Key Terms
12.7 Exercises

13.1 Uncertainty: Sources and Solutions
13.2 Fuzzy Rule-Based Systems
13.3 A Probability-Based Approach to Uncertainty
13.4 Chapter Summary
13.5 Key Terms
13.6 Exercises

14.1 Characteristics of Intelligent Agents
14.2 Types of Agents
14.3 Integrating Data Mining, Expert Systems, and Intelligent Agents
14.4 Chapter Summary
14.5 Key Terms
14.6 Exercises