Couple PCA plot and clusters to labels I am trying my first 'project' concerning machine learning and I am a bit stuck.
However, I am not sure if it's even possible but here goes my question.

What I want to achieve is clustering user groups based on the amount of visits a user does on a certain website.
So I started out with this feature matrix:

USER    abc.be  abc.be/a    abc.be/b    xyz.be  xyz.be/a
123      0        0           0            0      1
456      1        0           1            0      0
789      2        3           1            0      0
321      1        0           1            0      1
654      1        1           1            1      1
987      0        1           0            3      0


So I got in this example 5 features (my 5 different websites).
So then I used PCA to come to 2 dimensions, so I could plot it and see how it went.

My feature matrix (in my example) is 5 columns * 6 rows.

My PCA matrix is 2 columns * 6 rows.

I came to this plot (please note that this plot uses different data then the example but the idea is the same)



The green points are my PCA points
The red circles are my K-Means centroids.

But the part I am struggling with is this: so I got my clusters (red circles) but how can I use this to say:"Looks like most users go to either site A or site B)?

So how can I couple my clusters to a feature label from my feature matrix?

Or how does one approach this?

Any help is appreciated :)
So how can I couple my clusters to a feature label from my feature matrix?

Principal Components are not intuitive features. What seems common here is to cluster users based on PCs and investigate clusters based on original features afterward i.e. extract different clusters and plot data based on different subsets of features and use different colors for different clusters. It might give you some intution.

These two paths can be seen in many PCA results where the information varies with e.g time. 

For instance in your case a beginner user visits less than an old one and the number of new users are most probably higher than old ones so data will be more dense around origin and far it gets from the origin less dense it becomes. Such a phenomenon affects your PCs as well so you'll see some paths along different lines in PC space.

Hope it helps :)