Data Mining

Description

What Will I Get ?

Introduction To Data Mining

The theoretical concepts covered in this course are critical to building foundational knowledge of data mining.

Data Mining Terms and Definitions:

• The purpose of this module is to define the language that is used in Data Mining.

Statistics Overview:

• The purpose of this module is to cover the statistical concepts that are fundamental to the study of Data Mining. Data mining leverages statistics for cleaning and sorting data.

Similarity and dissimilarity:

• The purpose of this module is to describe the meaning behind Similarity and Dissimilarity in the context of Data Mining.

Cosine Similarity:

• The purpose of this module is to describe cosine similarity definitions and terminology.

Tanimoto calculation:

• The purpose of this module is to explain the meaning behind the tanimoto calculation.

Correlation analysis:

• The purpose of this module is to describe X² and it’s usage in correlation analysis.

Histograms:

• The purpose of this module is to explain histograms and their applications.

Discretization Techniques:

• The purpose behind this module is to describe the application of Entropy Based Discretization.

Segmentation by Natural partitioning:

• The purpose of this module is to explain the concept of Segmentation by Natural Partitioning.

Concept hierarchy:

• The purpose of this module is to explain how concept hierarchies are generated.

Bayesian Classification:

• The purpose of this module is to describe what Bayesian Classification is and how to apply it to training and test data.