[MRG] Clarified that DBSCAN is deterministic apart from data ordering (scikit-learn#7852)

Author: jbednar

doc/modules/clustering.rst

@@ -746,17 +746,18 @@ by black points below.
 
 .. topic:: Implementation
 
-    The algorithm is non-deterministic, but the core samples will
-    always belong to the same clusters (although the labels may be
-    different). The non-determinism comes from deciding to which cluster a
-    non-core sample belongs. A non-core sample can have a distance lower
-    than ``eps`` to two core samples in different clusters. By the
+    The DBSCAN algorithm is deterministic, always generating the same clusters 
+    when given the same data in the same order.  However, the results can differ when
+    data is provided in a different order. First, even though the core samples 
+    will always be assigned to the same clusters, the labels of those clusters
+    will depend on the order in which those samples are encountered in the data.
+    Second and more importantly, the clusters to which non-core samples are assigned
+    can differ depending on the data order.  This would happen when a non-core sample
+    has a distance lower than ``eps`` to two core samples in different clusters. By the
     triangular inequality, those two core samples must be more distant than
     ``eps`` from each other, or they would be in the same cluster. The non-core
-    sample is assigned to whichever cluster is generated first, where
-    the order is determined randomly. Other than the ordering of
-    the dataset, the algorithm is deterministic, making the results relatively
-    stable between runs on the same data.
+    sample is assigned to whichever cluster is generated first in a pass
+    through the data, and so the results will depend on the data ordering.
 
     The current implementation uses ball trees and kd-trees
     to determine the neighborhood of points,