Course Description

Outlines

• Course
• Paper
• Project
• Schedule of Presentations
• Description of Exam

Items of Interest

• Weka Software for Machine Learning and Data Mining

Site Map of Course Notes

1. KDD Process
   • The Primary Tasks of Data Mining
   • The Components of Data Mining Algorithms
   • Popular Data Mining Methods
2. Virtuous Cycle of Data Mining
3. Machine Learning Background
   • Inductive Inference
     • Version Space
       • Version Space Problem 1
       • Version Space Problem 2
     • Decision Trees
       • Decision Tree Construction
         • Decision Tree Problem, Part 1
       • Decision Tree Rules
         • Decision Tree Problem, Part 2
       • C4.5 Tutorial
         • C4.5 Example 1 - Golf
         • C4.5 Example 2 - Sunburn
         • C4.5 Example 3 - Homonyms
         • C4.5 Example 4 - Homonym Confusion Matrix
4. Confusion Matrix
   • ROC Graph
5. Cumulative Gains Chart and Lift Chart
6. Data Cubes
   • Iceberg Cubes
   • Generalization Space
   • Heuristic Measures of Interestingness
   • Principles for Measure of Interestingness
7. Itemsets
   • Support Measures
   • Apriori Itemset Generation
   • Share Measures
   • Another Example of Support and Share Measures
   • Apriori Program
   • Dynamic Itemsets
   • Dynamic Itemset Program
8. Probability Based Objective Interestingness Measures
9. Clustering
10. Rough Sets
    • Dealing with Noisy Data in the Rough Set Method
    • Comparison with Decision Trees

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Last updated September 28, 2009.
Copyright © 2000-9 by Howard Hamilton