I want to impute missing values of a dataset for machine learning (knn imputation). Is it better to scale and center the data before the imputation or afterwards?
Since the scaling and centering might rely on min and max values, in the first case the subsequent imputation might add new max / min values and tamper the scaled/centered data.
However, the imputation process might also profit from a scaled and centered dataset.
What do you think is better, and why?