October 23, 2012

Hierarchical clustering on Variablles

The study of variables follow our last study on the variables of our Principal Component Analysis. It was the first real interesting result in searching the understanding of our data. 
The actual case of the hierarchical agglomeration clustering offers, for the variables point of view, this dendrogram :

This dendogram gives an interesting that the best interesting point or number of classes is 5. Because this is the bigger jump on the dendograme that indicate the better force on the grouping possibility.
For the knowledge of this dendrogram and to understand this information , I give all the graphical result from 2 classes to 10 classes.
We can see that the better graphical choice is Five. With a good balance between classes, this is the appropriated choice.

Two classes
Three classes
Four classes
Five classes

Six classes

Seven classes

Height Classes

Nine Classes

Ten classes

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