2.3. Summary

<< Click to Display Table of Contents >>

Navigation:  2. Introduction to the PCA techniques >

2.3. Summary


To summarize, PCA is used to:

1.do dimensionality reduction.

2.create good « distances-definition ». We need a correct « distances-definition » so that the K-Mean algorithm works properly and delivers good segmentation models. Without a PCA, the classical « distances-definitions » base on Euclidean-distances are giving random weights to each concept, thereby producing a totally random and arbitrary segmentation.


As you can see, a correct methodology for a good segmentation analysis relies heavily on PCA analysis. Stardust is the only segmentation tool that is directly offering you, in a few mouse clicks, a complete « PCA analysis » in all the steps of the segmentation analysis.


We will now see how these two concepts are used inside StarDust to:

Produce a segmentation model

Describe each segments from a business perspective