I understand how the clustering algorithm k-means works and I can map any new point to any of the lusters using the predict function.
What I want to understand is: how can I describe the clusters?
For example, if I have three variables, x, y, and z, I would like to be able that, say, people in cluster 1 are those for which x is in between these values, unless y is that and then x is in between these other values, etc.
I understand that, if you have hundreds of variables, this becomes very difficult, is there some kind of "decision tree"-type of description I can use?
Alternatively, is there a way to output what the function kmeans derived by the algorithm, which I will use to predict any new point, looks like?