Update README for clustering
@@ -120,9 +120,10 @@ packages can manage.
Second, UMAP scales well in embedding dimension -- it isn't just for
visualisation! You can use UMAP as a general purpose dimension reduction
technique as a preliminary step to other machine learning tasks. With a
-little care (documentation on how to be careful is coming) it partners well
-with the `hdbscan <https://github.com/scikit-learn-contrib/hdbscan>`_
-clustering library.
+little care it partners well with the `hdbscan
+<https://github.com/scikit-learn-contrib/hdbscan>`_ clustering library (for
+more details please see `Using UMAP for Clustering
+<https://umap-learn.readthedocs.io/en/latest/clustering.html>`_).
Third, UMAP often performs better at preserving aspects of global structure of
the data than t-SNE. This means that it can often provide a better "big
