Hdbscan is an excellent technique to find the "optimal" number of clusters within your data when you have little a priori idea how many clusters should exist. This makes the method great for exploratory analysis:
Here's my problem: All results using hdbscan with the python implement in the link above rely on the crucial min_cluster_size
If users have a priori little idea how many clusters best fit the data, what is the correct approach above? Isn't there a metric one uses to decide what the optimal number of clusters is?