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Discriminant analysis is a statistical technique to classify objects into mutually exclusive and exhaustive groups based on a set of measurable object's features. Term discriminant analysis comes with many different names for difference field of study. It is also often called *pattern recognition* , *supervised learning*, or *supervised classification* . This tutorial gives overview about Linear Discriminant Analysis (LDA). If the number of classes is more than two, it is also sometimes called Multiple Discriminant Analysis (MDA). *You can download the MS Excel worksheet companion of this tutorial here*

*Click on the following topics below *

Purpose of discriminant Analysis

Linear Discriminant Analysis (LDA)

LDA formula (and Derivation of LDA formula)

Numerical example

Difference of Cluster Analysis and Discriminant Analysis

Example of Applications

Resources on Discriminant Analysis

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See Also:

Similarity and dissimilarity measurement (multivariate distance), K means clustering, K nearest neighbor algorithm, Clustering, Decision Tree
, Linear Algebra

**Preferable reference for this tutorial is**

Teknomo, Kardi (2015) Discriminant Analysis Tutorial. http://people.revoledu.com/kardi/ tutorial/LDA/