standardization/normalization for 1D clustering? I have two input variables revenue and age. Am trying to find different bins within that variables.
For ex: I have revenue and age.
I see that my revenue data is skewly distributed and regular methods like quantiles, binning etc cannot be applied due to skewness (and gives misleading results).
Is it a good practice to scale/normalize the 1d data before we apply techniques like jenks natural breaks??
Or we should standardize/nornalize only for k-means multivariate clustering algorithm?

Note that Jenks natural breaks optimization is 1D k-means, and scaling and shifting is done typically for bringing different features into one scale.  If there is only one feature, shifting and scaling won't change the resulting clusters if same (shifted and scaled as well) random initialization is used. But, you could apply other transformations, such as log transformation, to battle with skew and see if it helps with your cluster assignments.
