Do I need a test-train split for K-means clustering even if I'm not looking to predict anything?
No, in clustering (i.e unsupervised learning ) you do not need to split the data
I have a set of 2000 points which are basically x,y coordinates of pass origins from association football. I want to run a k-means clustering algorithm on it to just classify it to get which 10 passes are the most common (k=10). However, I don't want to predict any points for future values. I simply want to work with the existing data. Do I still need to split it into testing-training sets? I assume they're only done when we want to train the model on a particular set to calculate for future values (?)
I'm new to clustering (and Python as a whole) so any help would be appreciated.