Virtual University

Decision Tree Induction:Brief Review of Entropy (V-U)

Data reduction: Introduction (V-U)

Decision Tree Induction:Comparison of Attribute Selection Measures (V-U)

Data reduction: Linear regression (V-U)

Decision Tree Induction:Enhancements to Basic Decision Tree Induction (V-U)

Data reduction: Numerosity Reduction (V-U)

Data reduction: Numerosity Reduction using Clustering (V-U)

Decision Tree Induction:Over fitting and Tree Pruning (V-U)

Density-Based Clustering: Introduction (V-U)

Density-Based Methods: DBSCAN (V-U)

Density-Based Methods: Density based method parameters (V-U)

Errors (V-U)

Frequent Item set Apriori Methods (V-U)

Frequent Item set Mining Methods: Apriori: A Candidate Generation-and-Test Approach (V-U)

Frequent Item set Mining Methods: ECLAT: Frequent Pattern Mining with Vertical Data Format (V-U)

Frequent Item set Mining Methods: How to Count Supports of Candidates? (V-U)

Frequent Item set Mining Methods: Improving the Efficiency of Apriori (V-U)

Frequent Item set Mining Methods: Mining Close Frequent Patterns and Max patterns (V-U)

Gain ratio (V-U)

Gini index (V-U)

Grid based clustering (V-U)

Grid-Based Methods:Comments on STING (V-U)

Grid-Based Methods:WaveCluster: Clustering by Wavelet Analysis (1998) (V-U)

Hierarchical Methods: AGNES (Agglomerative Nesting) (V-U)

Hierarchical Methods: Introduction (V-U)