.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/cluster/plot_bisect_kmeans.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_bisect_kmeans.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_bisect_kmeans.py:


=============================================================
二分 K-Means 和常规 K-Means 性能比较
=============================================================

此示例展示了常规 K-Means 算法和二分 K-Means 之间的差异。

当增加 n_clusters 时，K-Means 聚类会有所不同，而二分 K-Means 聚类则建立在之前的聚类基础上。因此，它往往会创建具有更规则的大规模结构的聚类。这种差异可以直观地观察到：对于所有数量的聚类，二分 K-Means 都会有一条将整体数据云分成两部分的分割线，而常规 K-Means 则没有。

.. GENERATED FROM PYTHON SOURCE LINES 11-61



.. image-sg:: /auto_examples/cluster/images/sphx_glr_plot_bisect_kmeans_001.png
   :alt: Bisecting K-Means : 4 clusters, Bisecting K-Means : 8 clusters, Bisecting K-Means : 16 clusters, K-Means : 4 clusters, K-Means : 8 clusters, K-Means : 16 clusters
   :srcset: /auto_examples/cluster/images/sphx_glr_plot_bisect_kmeans_001.png
   :class: sphx-glr-single-img





.. code-block:: Python


    import matplotlib.pyplot as plt

    from sklearn.cluster import BisectingKMeans, KMeans
    from sklearn.datasets import make_blobs

    print(__doc__)


    # 生成样本数据
    n_samples = 10000
    random_state = 0

    X, _ = make_blobs(n_samples=n_samples, centers=2, random_state=random_state)

    # KMeans 和 BisectingKMeans 的聚类中心数量
    n_clusters_list = [4, 8, 16]

    # 算法比较
    clustering_algorithms = {
        "Bisecting K-Means": BisectingKMeans,
        "K-Means": KMeans,
    }

    # 为每个变体制作子图
    fig, axs = plt.subplots(
        len(clustering_algorithms), len(n_clusters_list), figsize=(12, 5)
    )

    axs = axs.T

    for i, (algorithm_name, Algorithm) in enumerate(clustering_algorithms.items()):
        for j, n_clusters in enumerate(n_clusters_list):
            algo = Algorithm(n_clusters=n_clusters, random_state=random_state, n_init=3)
            algo.fit(X)
            centers = algo.cluster_centers_

            axs[j, i].scatter(X[:, 0], X[:, 1], s=10, c=algo.labels_)
            axs[j, i].scatter(centers[:, 0], centers[:, 1], c="r", s=20)

            axs[j, i].set_title(f"{algorithm_name} : {n_clusters} clusters")


    # 隐藏顶部图的 x 标签和刻度标签以及右侧图的 y 刻度。
    for ax in axs.flat:
        ax.label_outer()
        ax.set_xticks([])
        ax.set_yticks([])

    plt.show()


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

   **Total running time of the script:** (0 minutes 0.841 seconds)


.. _sphx_glr_download_auto_examples_cluster_plot_bisect_kmeans.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_bisect_kmeans.ipynb
        :alt: Launch binder
        :width: 150 px

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

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

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

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

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

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


.. include:: plot_bisect_kmeans.recommendations


.. only:: html

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

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