Data Mining (V-U)

Outlier Analysis: Deviation-Based Approach for outleir discovery (V-U)

Model-Based Methods: Conceptual Clustering (V-U)

Model-Based Methods: EM — Expectation Maximization (V-U)

Outlier Analysis: Distance-Based Approach for outleir discovery (V-U)

Outlier Analysis: Statistical Approaches for outlier discovery (V-U)

Model-Based Methods: Introduction & examples (V-U)

Outlier Analysis: What Is Outlier Discovery? (V-U)

Model-Based Methods: Neural Network Approach & its advantages (V-U)

Model-Based Methods: Self-Organizing Feature Map (SOM) (V-U)

Partitioning Methods: Basic Concept (V-U)

Multi layered Meta-web Architecture (V-U)

Partitioning Methods: Discussion/Comments on the K-Means Method (V-U)

N gram probabilities (V-U)

Partitioning Methods: The K-Means Clustering Method (V-U)

Naive bayes advantages (V-U)

Partitioning Methods: The K-Medoids Clustering Method (V-U)

Naive Bayes and LM (V-U)

Partitioning Methods: Variations of the K-Means Method (V-U)

Rain forest example (V-U)

Ratio scaled variables (V-U)

Regular expressions (V-U)

Rule-Based Classification: Rule Induction: Sequential Covering Method (V-U)

Rule-Based Classification: Using IF-THEN Rules for Classification (V-U)

Rule-Based Classification:Rule Extraction from a Decision Tree (V-U)

Spell correction (V-U)