DOC Link OPTICS to user guide
@@ -34,6 +34,8 @@ def optics(X, min_samples=5, max_bound=np.inf, metric='euclidean',
clusters from them. Unlike DBSCAN, keeps cluster hierarchy for a variable
neighborhood radius. Optimized for usage on large point datasets.
+ Read more in the :ref:`User Guide <optics>`.
+
Parameters
----------
X : array, shape (n_samples, n_features)
@@ -100,6 +102,7 @@ def optics(X, min_samples=5, max_bound=np.inf, metric='euclidean',
algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, optional
Algorithm used to compute the nearest neighbors:
+
- 'ball_tree' will use :class:`BallTree`
- 'kd_tree' will use :class:`KDTree`
- 'brute' will use a brute-force search.
@@ -159,6 +162,8 @@ class OPTICS(BaseEstimator, ClusterMixin):
clusters from them. Unlike DBSCAN, keeps cluster hierarchy for a variable
neighborhood radius. Optimized for usage on large point datasets.
+ Read more in the :ref:`User Guide <optics>`.
+
Parameters
----------
min_samples : int
@@ -222,6 +227,7 @@ class OPTICS(BaseEstimator, ClusterMixin):
algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, optional
Algorithm used to compute the nearest neighbors:
+
- 'ball_tree' will use :class:`BallTree`
- 'kd_tree' will use :class:`KDTree`
- 'brute' will use a brute-force search.
