Data Mining (V-U)

A multi-dimensional data model: 3D data cube representation (V-U)

A multi-dimensional data model: Data Warehouse Design (V-U)

A multi-dimensional data model: Data warehouse snowflake schema (V-U)

A multi-dimensional data model: Fact Constellation Schema (V-U)

A multi-dimensional data model: Measures of Data Cube (V-U)

A multi-dimensional data model: Typical OLAP Operations (V-U)

Bag of words model (V-U)

Basic Concepts of Classification: A Two-Step Process (V-U)

Basic Statistical Descriptions of Data: Measuring the Central Tendency (V-U)

Basic Concepts of Classification: Introduction (V-U)

Basic Statistical Descriptions of Data: Measuring the Dispersion of Data (V-U)

Basic Concepts of Classification: Supervised vs. Unsupervised Learning (V-U)

Basic Statistical Descriptions of Data: Symmetric vs. Skewed Data (V-U)

Basic Concepts of Cluster Analysis: Challenges of Clutering (V-U)

Bayes Classification Methods: Naïve Bayes Classifier (V-U)

Basic Concepts of Cluster Analysis: Clustering Applications (V-U)

Bayes Classification Methods: Naïve Bayes Classifier: An Example (V-U)

Basic Concepts of Cluster Analysis: Clustering as a Preprocessing Tool (V-U)

Bigram based correction & its example (V-U)

Basic Concepts of Cluster Analysis: Clustering criteria (V-U)

BOAT and its advantages (V-U)

Basic Concepts of Cluster Analysis: Measure the Quality of Clustering (V-U)

Candidate generation (V-U)

Basic Concepts of Cluster Analysis: Quality: What Is Good Clustering? (V-U)

Chain rule (V-U)