For a specific task of score fusion I need to test my data on some different normalization techniques like typical Z-normalization or Sigmoid-normalization. This is my first step to do.
For a second step I need to have comparable data in the same data range like from 0 to 1.
I would ask myself right now, if the following is a possible (and probably recommended) process or is there some mistake. If so, is there any other solution? My current process:
1. Yes, normalisation does produce data with a different range i.e. new scores with a mean of 0 and a standard deviation of 1.
2. Scaling is a method used to standardize the range of independent variables and includes normalising the data using minimum and maximum scaling.
It is reasonable to do both (imo) because they are acceptable means of manipulating data :)
There is no point in unit variance normalisation before range scaling. Mean centre then apply each normalisation separately.
I wouldn't recommend min-max scaling, as it depends only on the extreme values (one value beeing a little off will distort the whole data set). It might be used after quantile-normalization, but using it with raw data, centered data or z-scores is risky.
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