I'm currently working in a problem, where I think a supervised clustering approach might be a good candidate, but I'm not sure and haven't really worked with such scenario before. Let me break it down:
I'm working with a supervised scenario: I have some financial data and an associated probability of risk derived from another model. What I'd like to do is use that probability as a label and run a clustering algorithm to categorize the data according "partly" to the associated risk. That is, I want the algorithm to do a good job both at clustering the data on related features (proximity), but with the constraint that the associated risk is similar.
There might be better approaches to what I have in mind, which is why I'd be very happy to get feedback or other suggested methods, thanks.