How to find the optimal number of clusters using k-prototype in python
Most evaluation methods need a distance matrix.
They will then work with mixed data, as long as you have a distance function that helps solving your problem. But they will not be very scalable.
I am trying to cluster some big data by using the k-prototypes algorithm. I am unable to use K-Means algorithm as I have both categorical and numeric data. Via k prototype clustering method I have been able to create clusters if I define what k value I want. 
How do I find the appropriate number of clusters for this.? 
Will the popular methods available (like elbow method and silhouette score method) with only the numerical data works out for mixed data?