K-modes clustering: Estimating which features were most impactful on clustering? I have entirely categorical data (survey results from users), so I've used k-modes clustering to better understand my users. 

I'm not an expert at clustering methods at all. Is there a way to known way of estimating the importance of a feature (or combination of features) in deciding which cluster a user falls into?
It boils down to counting.

Compute which feature value has the highest probability of predicting a particular cluster. It's a straightforward application of Bayes' formula.