# What is a good clustering fitness metric for DBSCAN?

Usually, my go-to goodness of fit for evaluating clustering (e.g., k-means) is the average silhouette.

However, for DBSCAN it doesn't work since there are lots of non-clustered points. So increasing the stringency of the parameters removes neighboring clusters, and therefore increases the difference between intra- and inter-cluster distance -- just because there are less points clustered.

What is a good clustering method for DBSCAN that takes into account this issue or is robust to number of points clusters?

• how about treating each non-clustered point as a single cluster? – dontloo Jul 31 '18 at 4:05

It discusses in detail the validation of density based clustering methods including the DBCV suggested earlier. It also includes a rich discussion on related works and experimental evaluation.