Is the k-center problem part of machine learning or computational geometry? I am currently working especially with the k-center problem, which is e.g. used to determine optimal locations for k warehouses. This is done by defining k circles that cover a given set of points (here for example inhabitants), and at the same time, the radii of the circles should be minimal. The centers of these k circles would then be the optimal locations for the warehouses.

The question arises whether this method belongs to machine learning or computational geometry. Since it is a clustering method I would actually think that it belongs to machine learning, more specifically to unsupervised learning. On the other hand, there are many algorithms to solve this problem which are based on computational geometry.

How about your opinion?
Where is the "learning"? Where is the "training", and "becoming better"?

It's an optimization problem, but optimization is everywhere.

Fits computational geometry much better than ML.

But as a rough guideline, check which books discuss this well. Can you name a book that belongs into machine learning that discusses this is sufficient depth?