HDBSCAN cluster: still unclear to me how to chose 'min_cluster_size` 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:

http://hdbscan.readthedocs.io/en/latest/comparing_clustering_algorithms.html

Here's my problem: All results using hdbscan with the python implement in the link above rely on the crucial min_cluster_size 

http://hdbscan.readthedocs.io/en/latest/parameter_selection.html 

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?
Optimal in which sense?

The crucial thing with clustering is that there is no optimal solution. Different solutions tell you a different part of the story. And to be able to get different views, you will need parameters. It is a exploratory technique.

Various attempts at defining "optimal" solutions have failed for practical use, just think of k-means.