I am using the knn means algorithm to distinguish groups. In case on the iris dataset, lets take this example:
from sklearn import datasets iris = datasets.load_iris() X = iris.data
I understand that I can create different clusters with k. What I am looking for however is a way that can help me to determine what is the best k, so whether 2,3,4 etc... creates the best and most homegenous groups?
Any thoughts on what is considered best practise in this case?