Course Description

Outlines

• Course
• Paper
• Project
• Description of Exam

Items of Interest

• Weka Software for Machine Learning and Data Mining
• InstallingSQLServer 2008

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
7. Itemsets
• Support Measures
• Apriori Itemset Generation
• Share Measures
• Another Example of Support and Share Measures
• Apriori Program
• Dynamic Itemsets
• Dynamic Itemset Program
8. Clustering
9. Rough Sets
   • Dealing with Noisy Data in the Rough Set Method
   • Comparison with Decision Trees

Written by: Howard Hamilton, Ergun Gurak, Leah Findlater, Wayne Olive, and James Ranson.
Last updated .
Copyright © 2000-7 by Howard J. Hamilton, Department of Computer Science, University of Regina, Regina, Sask., Canada S4S 0A2