This might not be a very good question, but I would still ask if it's beneficial to do EDA before running a clustering algorithm?
I understand that EDA helps us generate good and helpful insights into the data, which is crucial in data understanding. If we leave aside standard checks and manipulations like - removing outliers, scaling, removing constant value columns, removing null/'zero' value columns, etc. and if we have 20-30 features. How will EDA help me in producing good and sensible clusters? Is it even necessary to do the EDA before clustering?
Note: I am using k-means