Is there a way to do unsupervised clustering based on similarity (like all other methods), but create clusters by splitting on just one (or specific) features? For example, I have customer data with lots of features, but I want to find the best clustering of data by segmenting the data on specific bands of customer revenue like [\$100, \$1500, \$10000], etc.
I just want to make sure to compare apples to apples in the best possible way. I'm currently thinking a combination of hierarchical clustering (if there's multiple columns) and Random Forest (to split based on specific predictors). I've looked into Unsupervised Random Forest but it seems to just find a best way to cluster data based on similarity by transforming it, and not give how, or which columns they used to split the data.