PCA and Clustering
This tutorial introduces examples, including the analysis of handwritten digits, and then applies PCA to reduce the dimensionality of the data set. Observe how it connects with programming concepts introduced in the previous unit dealing with PCA.
Clustering
The main idea behind clustering is pretty straightforward. Basically, we say to ourselves, "I have these points here, and I can see that they organize into groups. It would be nice to describe these things more concretely, and, when a new point comes in, assign it to the correct group". This general idea encourages exploration and opens up a variety of algorithms for clustering.
*The examples of the outcomes from different algorithms from scikit-learn*
The algorithms listed below do not cover all the clustering methods out there, but they are the most commonly used ones.