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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