I'm currently working with a large multivariate data set where I plan to use K-Means to try to find any associations in the data.
I'm not particularly well-versed when it comes to statistics, though I did realize I needed to exclude outliers from my dataset.
Assuming I have a 3 numeric variable dataset, would it be correct to:
- Just scale the data and remove outliers from there, and then K-Means.
- Or scale the data, remove outliers, then use the normal data set now excluded of the outliers and then K-Means.
Essentially the difference between the two is that in one I am working with a scaled dataset, and another I am working with just the normal data. Both are removed of outliers > 2 standard deviations out.