DOC Fix docs for clustering.rst following doc guideline
@@ -19,11 +19,11 @@ data can be found in the ``labels_`` attribute.
One important thing to note is that the algorithms implemented in
this module can take different kinds of matrix as input. All the
- methods accept standard data matrices of shape ``[n_samples, n_features]``.
+ methods accept standard data matrices of shape ``(n_samples, n_features)``.
These can be obtained from the classes in the :mod:`sklearn.feature_extraction`
module. For :class:`AffinityPropagation`, :class:`SpectralClustering`
and :class:`DBSCAN` one can also input similarity matrices of shape
- ``[n_samples, n_samples]``. These can be obtained from the functions
+ ``(n_samples, n_samples)``. These can be obtained from the functions
in the :mod:`sklearn.metrics.pairwise` module.
Overview of clustering methods
@@ -821,7 +821,7 @@ by black points below.
This implementation is by default not memory efficient because it constructs
a full pairwise similarity matrix in the case where kd-trees or ball-trees cannot
- be used (e.g., with sparse matrices). This matrix will consume n^2 floats.
+ be used (e.g., with sparse matrices). This matrix will consume :math:`n^2` floats.
A couple of mechanisms for getting around this are:
- Use :ref:`OPTICS <optics>` clustering in conjunction with the
@@ -926,8 +926,8 @@ represented as children of a larger parent cluster.
`HDBSCAN <https://hdbscan.readthedocs.io>`_. The HDBSCAN implementation is
multithreaded, and has better algorithmic runtime complexity than OPTICS,
at the cost of worse memory scaling. For extremely large datasets that
- exhaust system memory using HDBSCAN, OPTICS will maintain *n* (as opposed
- to *n^2*) memory scaling; however, tuning of the ``max_eps`` parameter
+ exhaust system memory using HDBSCAN, OPTICS will maintain :math:`n` (as opposed
+ to :math:`n^2`) memory scaling; however, tuning of the ``max_eps`` parameter
will likely need to be used to give a solution in a reasonable amount of
wall time.
@@ -953,7 +953,7 @@ The CF Subclusters hold the necessary information for clustering which prevents
the need to hold the entire input data in memory. This information includes:
- Number of samples in a subcluster.
-- Linear Sum - A n-dimensional vector holding the sum of all samples
+- Linear Sum - An n-dimensional vector holding the sum of all samples
- Squared Sum - Sum of the squared L2 norm of all samples.
- Centroids - To avoid recalculation linear sum / n_samples.
- Squared norm of the centroids.
