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| Preface | |
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| Data Mining Fundamentals | |
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| Data Mining: A First View | |
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| Data Mining: A Definition | |
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| What Can Computers Learn? | |
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| Is Data Mining Appropriate for my Problem? | |
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| Expert Systems or Data Mining? | |
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| A Simple Data Mining Process Model | |
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| Why not Simple Search? | |
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| Data Mining Applications | |
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| Data Mining: A Closer Look | |
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| Data Mining Strategies | |
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| Supervised Data Mining Techniques | |
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| Association Rules | |
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| Clustering Techniques | |
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| Evaluating Performance | |
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| Basic Data Mining Techniques | |
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| Decision Trees | |
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| Generating Association Rules | |
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| The K-Means Algorithm | |
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| Genetic Learning | |
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| Choosing a Data Mining Technique | |
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| An Excel-Based Data Mining Tool | |
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| The iData Analyzer | |
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| ESX: A Multipurpose Tool for Data Mining | |
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| iDAV Format for Data Mining | |
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| A Five-Step Approach for Unsupervised Clustering | |
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| A Six-Step Approach for Supervised Learning | |
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| Techniques for Generating Rules | |
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| Instance Typicality | |
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| Special Considerations and Features | |
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| Tools For Knowledge Discovery | |
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| Knowledge Discovery in Databases | |
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| A KDD Process Model | |
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| Goal Identification | |
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| Creating a Target Data Set | |
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| Data Preprocessing | |
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| Data Transformation | |
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| Data Mining | |
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| Interpretation and Evaluation | |
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| Taking Action | |
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| The CRISP-DM Process Model | |
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| Experimenting with ESX | |
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| The Data Warehouse | |
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| Operational Databases | |
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| Data Warehouse Design | |
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| On-line Analytical Processing (OLAP) | |
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| Excel Pivot Tables for Data Analysis | |
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| Formal Evaluation Techniques | |
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| What Should be Evaluated? | |
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| Tools for Evaluation | |
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| Computing Test Set Confidence Intervals | |
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| Comparing Supervised Learner Models | |
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| Attrtribute Evaluation | |
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| Unsupervised Evaluation Techniques | |
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| Evaluating Supervised Models with Numeric Output | |
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| Advanced Data Mining Techniques | |
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| Neural Networks | |
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| Feed-Forward Neural Networks | |
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| Neural Network Training: A Conceptual View | |
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| Neural Network Explanation | |
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| General Considerations | |
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| Neural Network Learning: A Detailed View | |
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| Building Neural Networks with iDA | |
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| A Four-Step Approach for Backpropagation Learning | |
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| A Four-Step Approach for Neural Network Clustering | |
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| ESX for Neural Network Cluster Analysis | |
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| Statistical Techniques | |
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| Linear Regression Analysis | |
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| Logistic Regression | |
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| Bayes Classifier | |
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| Clustering Algorithms | |
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| Heuristics or Statistics? | |
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| Specialized Techniques | |
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| Time-Series Analysis | |
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| Mining the Web | |
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| Mining Textual Data | |
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| Improving Performance | |
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| Intelligent Systems | |
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| Rule-Based Systems | |
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| Exploring Artificial Intelligence | |
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| Problem Solving as a State Space Search | |
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| Expert Systems | |
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| Structuring a Rule-Based System | |
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| Managing Uncertainty in Rule-Based Systems | |
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| Uncertainty: Sources and Solutions | |
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| Fuzzy Rule-Based Systems | |
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| A Probability-Based Approach to Uncertainty | |
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| Intelligent Agents | |
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| Characteristics of Intelligent Agents | |
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| Types of Agents | |
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| Integrating Data Mining, Expert Systems, and Intelligent Agents | |
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| Appendix | |
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| Software Installation | |
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| Datasets for Data Mining | |
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| Decision Tree Attribute Selection | |
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| Statistics for Performance Evaluation | |
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| Excel 97 Pivot | |
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| Tabl | |