Unsupervised clustering with unknown number of clusters

Question

I have a large set of vectors in 3 dimensions. I need to cluster these based on Euclidean distance such that all the vectors in any particular cluster have a Euclidean dista

Answer 1

Use OPTICS, which works well with large data sets.

OPTICS: Ordering Points To Identify the Clustering Structure Closely related to DBSCAN, finds core sample of high density and expands clusters from them 1. Unlike DBSCAN, keeps cluster hierarchy for a variable neighborhood radius. Better suited for usage on large datasets than the current sklearn implementation of DBSCAN

from sklearn.cluster import OPTICS
db = OPTICS(eps=3, min_samples=30).fit(X)

Fine tune eps, min_samples as per your requirement.