# 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:

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?

• Define "fit the data". min_cluster_size=1` supposedly is the "best fit" yet subjectively usually much worse. There exists no objective notion of "best" that people find useful... – Has QUIT--Anony-Mousse Feb 7 '17 at 22:03