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 :)