.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/cluster/plot_digits_linkage.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_cluster_plot_digits_linkage.py>`
        to download the full example code. or to run this example in your browser via Binder

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_cluster_plot_digits_linkage.py:


=============================================================================
二维嵌入数字的各种凝聚聚类
=============================================================================

在数字数据集的二维嵌入上展示各种凝聚聚类的链接选项。

此示例的目的是直观地展示度量的行为，而不是为数字数据集找到好的聚类。这就是为什么示例在二维嵌入上进行。

这个示例向我们展示了凝聚聚类的“富者愈富”行为，倾向于创建不均匀的聚类大小。

这种行为在平均链接策略中尤为明显，最终会形成几个数据点较少的聚类。

单链接的情况甚至更为病态，形成一个覆盖大多数数字的非常大的聚类，一个中等大小（干净）的聚类包含大多数零数字，其他所有聚类则由边缘噪声点组成。

其他链接策略导致更均匀分布的聚类，因此更不容易受到数据集随机重采样的影响。

.. GENERATED FROM PYTHON SOURCE LINES 19-79



.. rst-class:: sphx-glr-horizontal


    *

      .. image-sg:: /auto_examples/cluster/images/sphx_glr_plot_digits_linkage_001.png
         :alt: ward linkage
         :srcset: /auto_examples/cluster/images/sphx_glr_plot_digits_linkage_001.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /auto_examples/cluster/images/sphx_glr_plot_digits_linkage_002.png
         :alt: average linkage
         :srcset: /auto_examples/cluster/images/sphx_glr_plot_digits_linkage_002.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /auto_examples/cluster/images/sphx_glr_plot_digits_linkage_003.png
         :alt: complete linkage
         :srcset: /auto_examples/cluster/images/sphx_glr_plot_digits_linkage_003.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /auto_examples/cluster/images/sphx_glr_plot_digits_linkage_004.png
         :alt: single linkage
         :srcset: /auto_examples/cluster/images/sphx_glr_plot_digits_linkage_004.png
         :class: sphx-glr-multi-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Computing embedding
    Done.
    ward :  0.04s
    average :       0.02s
    complete :      0.02s
    single :        0.04s






|

.. code-block:: Python


    # 作者：scikit-learn 开发者
    # SPDX-License-Identifier: BSD-3-Clause

    from time import time

    import numpy as np
    from matplotlib import pyplot as plt

    from sklearn import datasets, manifold

    digits = datasets.load_digits()
    X, y = digits.data, digits.target
    n_samples, n_features = X.shape

    np.random.seed(0)


    # ----------------------------------------------------------------------
    # 可视化聚类
    def plot_clustering(X_red, labels, title=None):
        x_min, x_max = np.min(X_red, axis=0), np.max(X_red, axis=0)
        X_red = (X_red - x_min) / (x_max - x_min)

        plt.figure(figsize=(6, 4))
        for digit in digits.target_names:
            plt.scatter(
                *X_red[y == digit].T,
                marker=f"${digit}$",
                s=50,
                c=plt.cm.nipy_spectral(labels[y == digit] / 10),
                alpha=0.5,
            )

        plt.xticks([])
        plt.yticks([])
        if title is not None:
            plt.title(title, size=17)
        plt.axis("off")
        plt.tight_layout(rect=[0, 0.03, 1, 0.95])


    # ----------------------------------------------------------------------
    # 数字数据集的二维嵌入
    print("Computing embedding")
    X_red = manifold.SpectralEmbedding(n_components=2).fit_transform(X)
    print("Done.")

    from sklearn.cluster import AgglomerativeClustering

    for linkage in ("ward", "average", "complete", "single"):
        clustering = AgglomerativeClustering(linkage=linkage, n_clusters=10)
        t0 = time()
        clustering.fit(X_red)
        print("%s :\t%.2fs" % (linkage, time() - t0))

        plot_clustering(X_red, clustering.labels_, "%s linkage" % linkage)


    plt.show()


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 1.114 seconds)


.. _sphx_glr_download_auto_examples_cluster_plot_digits_linkage.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: binder-badge

      .. image:: images/binder_badge_logo.svg
        :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/cluster/plot_digits_linkage.ipynb
        :alt: Launch binder
        :width: 150 px

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_digits_linkage.ipynb <plot_digits_linkage.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_digits_linkage.py <plot_digits_linkage.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: plot_digits_linkage.zip <plot_digits_linkage.zip>`


.. include:: plot_digits_linkage.recommendations


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_