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DOC add link plot_inductive_clustering #30158

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add link plot_inductive_clustering
claudio1975 Oct 26, 2024
dda185a
update link plot_inductive_clustering
claudio1975 Oct 30, 2024
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9 changes: 7 additions & 2 deletions doc/modules/clustering.rst
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Expand Up @@ -137,8 +137,13 @@ mixture models. KMeans can be seen as a special case of Gaussian mixture
model with equal covariance per component.

:term:`Transductive <transductive>` clustering methods (in contrast to
:term:`inductive` clustering methods) are not designed to be applied to new,
unseen data.
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This marks the key difference between Transductive and Inductive Clustering. Therefore, I think it should be preseverd.

:term:`inductive` clustering methods) are not
designed to be applied to new, unseen data.
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Suggested change
:term:`inductive` clustering methods) are not
designed to be applied to new, unseen data.
:term:`inductive` clustering methods) are not designed to be applied to new,
unseen data.

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Hi @virchan thanks for the suggestions.
I've seen yesterday that when I launched the updated PR, it started directly from the own repository instead from my fork repository.
I received the notifications about issues on the CI failed tests, and for instance I report the comment from the linter step:
""
Error: Unable to download artifact(s): Artifact not found for name: lint-log
Please ensure that your artifact is not expired and the artifact was uploaded using a compatible version of toolkit/upload-artifact.
""
Maybe could come from not updated files in the own repository?
To avoid troubles, I close the pull request, I'll update my fork and local repository, and then I'll open a new one.


.. rubric:: Examples

* :ref:`sphx_glr_auto_examples_cluster_plot_inductive_clustering.py`: A practical
example of inductive clustering model.
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* :ref:`sphx_glr_auto_examples_cluster_plot_inductive_clustering.py`: A practical
example of inductive clustering model.
* :ref:`sphx_glr_auto_examples_cluster_plot_inductive_clustering.py`: An example
of an inductive clustering model for handling new data.


.. _k_means:

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