There are different ways of scaling variables in k-means clustering. I can divide all variable by sum of that column, I can take z score,I can have (max(var)-value)/(max(var)-min(var) etc.
How selection of variable scaling/normalising would change clustering result? I have done clustering on 1 million rows with 20 continuous variables. And the wss/tss is different for different scaling methods.
What is the reason from Algo perspective ?