Clustering versus Discriminant Analysis
|
About |
Cluster Analysis |
Discriminant Analysis |
|
Other name |
Unsupervised learning |
Supervised learning |
|
Training or learning period |
Object category is unknown Rule of classification is given (generalized distance based) |
Object category is known |
|
Purposes of training |
To know category of each object |
To know the classification rule |
|
After training (usage) |
To classify object into a number of category |
To classify object into a number of category |
In clustering, the category of the object is unknown. However, we know the rule to classify (usually based on distance) and we also know the features (independent variables) that can describe the classification of the object. There is no training example to examine whether the classification is correct or not. Thus, the objects are assigned into groups merely based on the given rule.
In discriminant analysis, object groups and several training examples of objects that have been grouped are known. The model of classification is also given (for example, linear or quadratic) and we want to know the best fit parameters of the model that can best separate the objects based on the training samples.
The differences between clustering and discriminant analysis are only on the training session. After the parameters are determined, and we start to use the model, both models have the same usage to classify object into a number of category
See Also:
Similarity and dissimilarity measurement
(multivariate distance),
K means clustering
,
K nearest neighbor algorithm
,
Clustering
,
Decision Tree
This tutorial is copyrighted .
Preferable reference for this tutorial is
Teknomo, Kardi (2015) Discriminant Analysis Tutorial. http://people.revoledu.com/kardi/ tutorial/LDA/