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DOC update documentation for DBSCAN and OPTICS #21343

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21 changes: 11 additions & 10 deletions doc/modules/clustering.rst
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Expand Up @@ -89,14 +89,15 @@ Overview of clustering methods
* - :ref:`DBSCAN <dbscan>`
- neighborhood size
- Very large ``n_samples``, medium ``n_clusters``
- Non-flat geometry, uneven cluster sizes, transductive
- Non-flat geometry, uneven cluster sizes, outlier removal,
transductive
- Distances between nearest points

* - :ref:`OPTICS <optics>`
- minimum cluster membership
- Very large ``n_samples``, large ``n_clusters``
- Non-flat geometry, uneven cluster sizes, variable cluster density,
transductive
outlier removal, transductive
- Distances between points

* - :ref:`Gaussian mixtures <mixture>`
Expand Down Expand Up @@ -203,9 +204,9 @@ initializations of the centroids. One method to help address this issue is the
k-means++ initialization scheme, which has been implemented in scikit-learn
(use the ``init='k-means++'`` parameter). This initializes the centroids to be
(generally) distant from each other, leading to probably better results than
random initialization, as shown in the reference.
random initialization, as shown in the reference.

K-means++ can also be called independently to select seeds for other
K-means++ can also be called independently to select seeds for other
clustering algorithms, see :func:`sklearn.cluster.kmeans_plusplus` for details
and example usage.

Expand Down Expand Up @@ -1383,7 +1384,7 @@ more broadly common names.

* `Wikipedia entry for the Adjusted Mutual Information
<https://en.wikipedia.org/wiki/Adjusted_Mutual_Information>`_

.. [VEB2009] Vinh, Epps, and Bailey, (2009). "Information theoretic measures
for clusterings comparison". Proceedings of the 26th Annual International
Conference on Machine Learning - ICML '09.
Expand All @@ -1394,13 +1395,13 @@ more broadly common names.
Clusterings Comparison: Variants, Properties, Normalization and
Correction for Chance". JMLR
<http://jmlr.csail.mit.edu/papers/volume11/vinh10a/vinh10a.pdf>

.. [YAT2016] Yang, Algesheimer, and Tessone, (2016). "A comparative analysis of
community
detection algorithms on artificial networks". Scientific Reports 6: 30750.
`doi:10.1038/srep30750 <https://www.nature.com/articles/srep30750>`_.



.. _homogeneity_completeness:

Expand Down Expand Up @@ -1738,8 +1739,8 @@ Calinski-Harabasz Index


If the ground truth labels are not known, the Calinski-Harabasz index
(:func:`sklearn.metrics.calinski_harabasz_score`) - also known as the Variance
Ratio Criterion - can be used to evaluate the model, where a higher
(:func:`sklearn.metrics.calinski_harabasz_score`) - also known as the Variance
Ratio Criterion - can be used to evaluate the model, where a higher
Calinski-Harabasz score relates to a model with better defined clusters.

The index is the ratio of the sum of between-clusters dispersion and of
Expand Down