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Update index.rst
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.readthedocs.yaml

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sphinx:
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configuration: docs/conf.py
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build:
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os: ubuntu-22.04
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tools:
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python: "3.8"
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python:
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version: 3.8
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install:
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- requirements: docs/requirements.txt

README.md

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@@ -37,7 +37,7 @@ For further details on medoid Silhouette clustering with automatic cluster numbe
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> Lars Lenssen, Erich Schubert:
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> **Medoid silhouette clustering with automatic cluster number selection**
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> Information Systems (120), 2024, 102290
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> Information Systems (120), 2024, 102290
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> <https://doi.org/10.1016/j.is.2023.102290>
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an earlier version was published as:

docs/index.rst

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X, _ = fetch_openml('mnist_784', version=1, return_X_y=True, as_frame=False)
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X = X[:10000]
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diss = euclidean_distances(X)
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bk = kmedoids.bestk(diss, 100)
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print("Optimal number of clusters according to the Medoid Silhouette:", bk.bestk)
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print("Medoid Silhouette over range of k:", bk.losses)
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print("Range of k:", bk.rangek)
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dm = kmedoids.dynmsc(diss, 100)
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print("Optimal number of clusters according to the Medoid Silhouette:", dm.bestk)
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print("Medoid Silhouette over range of k:", dm.losses)
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print("Range of k:", dm.rangek)
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Memory Requirements
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-------------------
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* :ref:`FasterMSC<fastermsc>` (Lenssen and Schubert, 2022)
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* :ref:`FastMSC<fastmsc>` (Lenssen and Schubert, 2022)
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* :ref:`DynMSC<dynmsc>` (Lenssen and Schubert, 2023)
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* :ref:`Bestk<bestk>` (Lenssen and Schubert, 2023)
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* :ref:`PAMSIL<pamsil>` (Van der Laan and Pollard, 2003)
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* :ref:`PAMMEDSIL<pammedsil>` (Van der Laan and Pollard, 2003)
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* :ref:`Medoid Silhouette<medoid_silhouette>` (Van der Laan and Pollard, 2003)
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* :ref:`MedoidSilhouette<medoid_silhouette>` (Van der Laan and Pollard, 2003)
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Note that the k-means style "alternating" algorithm yields rather poor result quality
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(see Schubert and Rousseeuw 2021 for an example and explanation).
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.. autofunction:: dynmsc
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.. _Bestk:
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DynMSC
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=========
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.. autofunction:: bestk
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.. _PAMSIL:
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PAMSIL

docs/requirements.txt

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kmedoids
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scikit-learn
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sphinx-rtd-theme

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