# Does the choice of normalization change dramatically the result of a KMeans

I'm using a KMeans to get the profile of several users according to several columns (I'm working with RStudio).

To analyze my clusters, I decided to realize a radar chart, so I decided to use feature scaling : x-min(x)/diff(range(x)), to have my values in [0,1] (to get a quite good idea of my data per cluster). However, since there are multiple choice for normalization, I was wondering if doing my analysis with another choice for normalization - for instance : x-mean(x)/sd(x) - would give me the same results (in a general way at least)

Or am I completly wrong for considering my scaled data and should I use my unscaled data in my radar chart ?

• I would standardize the data first before running K-Means. This post provides some illustrative example! Oct 31, 2017 at 23:02
• Yes, it affects the result. There's no right way, and that's one problem with clustering.
– Emre
Nov 1, 2017 at 0:07