Using DBSCAN to find data that are far from high density clusters?
For anomaly detection use an anomaly detection algorithm instead.
There are siblings derived from DBSCAN for exactly this purpose. For example, LOF and LoOP and kNN and ...
Conscious that dbscan clusters don't necessarily have cluster centres, but for an anomaly detection task, I want to spot data that are outliers/away from the normal clusters. Is there a way to do this using sklearn's dbscan?