Update README.rst

Doc on return values

README.rst

@@ -25,15 +25,14 @@ Installation
     python3 setup.py install
 
 A projects allowing scalable hierarchical clustering, thanks to an
-approximated version of OPTICS, on arbitrary data and distance measures.
+approximated version of HDBSCAN, on arbitrary data and distance measures.
 
 Quickstart
 ----------
 
-Look at the HDBSCAN documentation for the meaning of the return values
-of the `cluster` method.  There are plenty of configuration options,
-inherited by HNSWs and HDBSCAN, but the only compulsory argument is a
-dissimilarity function between arbitrary data elements::
+There are plenty of configuration options, inherited by HNSWs and HDBSCAN,
+but the only compulsory argument is a dissimilarity function between arbitrary
+data elements::
 
     import flexible_clustering
     
@@ -50,6 +49,37 @@ dissimilarity function between arbitrary data elements::
 Make sure to run everything from *outside* the source directory, to
 avoid confusing Python path.
 
+Return Values
+-------------
+
+From the `HDBSCAN source code <https://hdbscan.readthedocs.io/en/latest/_modules/hdbscan/hdbscan_.html>`_:
+
+labels : ndarray, shape (n_samples, )
+        Cluster labels for each point.  Noisy samples are given the label -1.
+
+    probabilities : ndarray, shape (n_samples, )
+        Cluster membership strengths for each point. Noisy samples are assigned
+        0.
+
+    cluster_persistence : array, shape  (n_clusters, )
+        A score of how persistent each cluster is. A score of 1.0 represents
+        a perfectly stable cluster that persists over all distance scales,
+        while a score of 0.0 represents a perfectly ephemeral cluster. These
+        scores can be guage the relative coherence of the clusters output
+        by the algorithm.
+
+    condensed_tree : record array
+        The condensed cluster hierarchy used to generate clusters.
+
+    single_linkage_tree : ndarray, shape (n_samples - 1, 4)
+        The single linkage tree produced during clustering in scipy
+        hierarchical clustering format
+        (see http://docs.scipy.org/doc/scipy/reference/cluster.hierarchy.html).
+
+    min_spanning_tree : ndarray, shape (n_samples - 1, 3)
+        The minimum spanning as an edgelist. If gen_min_span_tree was False
+        this will be None.
+
 Demo/Example
 ------------