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DOC Add links to example plot_kmeans_stability_low_dim_dense.py #30349

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DOC Add links to example plot_kmeans_stability_low_dim_dense.py #30349

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yuanx749 Nov 26, 2024
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Update sklearn/cluster/_kmeans.py
yuanx749 Nov 26, 2024
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Update doc/modules/clustering.rst
yuanx749 Nov 26, 2024
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Add link with context
yuanx749 Nov 26, 2024
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7 changes: 4 additions & 3 deletions doc/modules/clustering.rst
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Expand Up @@ -222,9 +222,10 @@ 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. For a detailed example of
comaparing different initialization schemes, refer to
:ref:`sphx_glr_auto_examples_cluster_plot_kmeans_digits.py`.
random initialization, as shown in the reference. For detailed examples of
comparing different initialization schemes, refer to
:ref:`sphx_glr_auto_examples_cluster_plot_kmeans_digits.py` and
:ref:`sphx_glr_auto_examples_cluster_plot_kmeans_stability_low_dim_dense.py`.

K-means++ can also be called independently to select seeds for other
clustering algorithms, see :func:`sklearn.cluster.kmeans_plusplus` for details
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10 changes: 8 additions & 2 deletions sklearn/cluster/_kmeans.py
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Expand Up @@ -1213,8 +1213,11 @@ class KMeans(_BaseKMeans):
* If a callable is passed, it should take arguments X, n_clusters and a\
random state and return an initialization.

For an example of how to use the different `init` strategy, see the example
entitled :ref:`sphx_glr_auto_examples_cluster_plot_kmeans_digits.py`.
For an example of how to use the different `init` strategies, see
:ref:`sphx_glr_auto_examples_cluster_plot_kmeans_digits.py`.

For an evaluation of the impact of initialization, see the example
:ref:`sphx_glr_auto_examples_cluster_plot_kmeans_stability_low_dim_dense.py`.

n_init : 'auto' or int, default='auto'
Number of times the k-means algorithm is run with different centroid
Expand Down Expand Up @@ -1700,6 +1703,9 @@ class MiniBatchKMeans(_BaseKMeans):
If a callable is passed, it should take arguments X, n_clusters and a
random state and return an initialization.

For an evaluation of the impact of initialization, see the example
:ref:`sphx_glr_auto_examples_cluster_plot_kmeans_stability_low_dim_dense.py`.

max_iter : int, default=100
Maximum number of iterations over the complete dataset before
stopping independently of any early stopping criterion heuristics.
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