Data Mining: A Tutorial Based Primer: United States Edition

This primer on data mining provides an introduction to the principles and techniques for extracting information from a business-minded perspective. A basic familiarity with the field of data mining concepts is built and then enhanced via 13 data mining tutorials. Upon completion of these tutorials, students will be fully able to data mine. This book is appropriate for students of CS, MIS, and Information Technology.

Table of Contents:
(Each Chapter concludes with a Chapter Summary, Key Terms, and Exercises.) Preface. I. DATA MINING FUNDAMENTALS. 1. Data Mining: A First View. Data Mining: A Definition. What Can Computers Learn? Is Data Mining Appropriate for my Problem? Expert Systems or Data Mining? A Simple Data Mining Process Model. Why not Simple Search? Data Mining Applications. 2. Data Mining: A Closer Look. Data Mining Strategies. Supervised Data Mining Techniques. Association Rules. Clustering Techniques. Evaluating Performance. 3. Basic Data Mining Techniques. Decision Trees. Generating Association Rules. The K-Means Algorithm. Genetic Learning. Choosing a Data Mining Technique. 4. An Excel-Based Data Mining Tool. The iData Analyzer. ESX: A Multipurpose Tool for Data Mining. iDAV Format for Data Mining. A Five-Step Approach for Unsupervised Clustering. A Six-Step Approach for Supervised Learning. Techniques for Generating Rules. Instance Typicality. Special Considerations and Features. II. TOOLS FOR KNOWLEDGE DISCOVERY. 5. Knowledge Discovery in Databases. A KDD Process Model. Step 1: Goal Identification. Step 2: Creating a Target Data Set. Step 3: Data Preprocessing. Step 4: Data Transformation. Step 5: Data Mining. Step 6: Interpretation and Evaluation. Step 7: Taking Action. The CRISP-DM Process Model. Experimenting with ESX. 6. The Data Warehouse. Operational Databases. Data Warehouse Design. On-line Analytical Processing (OLAP). Excel Pivot Tables for Data Analysis. 7. Formal Evaluation Techniques. What Should be Evaluated? Tools for Evaluation. Computing Test Set Confidence Intervals. Comparing Supervised Learner Models. Attribute Evaluation. Unsupervised Evaluation Techniques. Evaluating Supervised Models with Numeric Output. III. ADVANCED DATA MINING TECHNIQUES. 8. Neural Networks. Feed-Forward Neural Networks. Neural Network Training: A Conceptual View. Neural Network Explanation. General Considerations. Neural Network Learning: A Detailed View. 9. Building Neural Networks with iDA. A Four-Step Approach for Backpropagation Learning. A Four-Step Approach for Neural Network Clustering. ESX for Neural Network Cluster Analysis. 10. Statistical Techniques. Linear Regression Analysis. Logistic Regression. Bayes Classifier. Clustering Algorithms. Heuristics or Statistics? 11. Specialized Techniques. Time-Series Analysis. Mining the Web. Mining Textual Data. Improving Performance. IV. INTELLIGENT SYSTEMS. 12. Rule-Based Systems. Exploring Artificial Intelligence. Problem Solving as a State Space Search. Expert Systems. Structuring a Rule-Based System. 13. Managing Uncertainty in Rule-Based Systems. Uncertainty: Sources and Solutions. Fuzzy Rule-Based Systems. A Probability-Based Approach to Uncertainty. 14. Intelligent Agents. Characteristics of Intelligent Agents. Types of Agents. Integrating Data Mining, Expert Systems, and Intelligent Agents. Appendix. Appendix A: Software Installation. Appendix B: Datasets for Data Mining. Appendix C: Decision Tree Attribute Selection. Appendix D: Statistics for Performance Evaluation. Appendix E: Excel 97 Pivot Tables. Bibliography.