.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/cluster/plot_agglomerative_dendrogram.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_agglomerative_dendrogram.py>`
        to download the full example code or to run this example in your browser via JupyterLite or Binder

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

.. _sphx_glr_auto_examples_cluster_plot_agglomerative_dendrogram.py:


=========================================
Plot Hierarchical Clustering Dendrogram
=========================================
This example plots the corresponding dendrogram of a hierarchical clustering
using AgglomerativeClustering and the dendrogram method available in scipy.

.. GENERATED FROM PYTHON SOURCE LINES 11-55



.. image-sg:: /auto_examples/cluster/images/sphx_glr_plot_agglomerative_dendrogram_001.png
   :alt: Hierarchical Clustering Dendrogram
   :srcset: /auto_examples/cluster/images/sphx_glr_plot_agglomerative_dendrogram_001.png
   :class: sphx-glr-single-img





.. code-block:: Python


    import numpy as np
    from matplotlib import pyplot as plt
    from scipy.cluster.hierarchy import dendrogram

    from sklearn.cluster import AgglomerativeClustering
    from sklearn.datasets import load_iris


    def plot_dendrogram(model, **kwargs):
        # Create linkage matrix and then plot the dendrogram

        # create the counts of samples under each node
        counts = np.zeros(model.children_.shape[0])
        n_samples = len(model.labels_)
        for i, merge in enumerate(model.children_):
            current_count = 0
            for child_idx in merge:
                if child_idx < n_samples:
                    current_count += 1  # leaf node
                else:
                    current_count += counts[child_idx - n_samples]
            counts[i] = current_count

        linkage_matrix = np.column_stack(
            [model.children_, model.distances_, counts]
        ).astype(float)

        # Plot the corresponding dendrogram
        dendrogram(linkage_matrix, **kwargs)


    iris = load_iris()
    X = iris.data

    # setting distance_threshold=0 ensures we compute the full tree.
    model = AgglomerativeClustering(distance_threshold=0, n_clusters=None)

    model = model.fit(X)
    plt.title("Hierarchical Clustering Dendrogram")
    # plot the top three levels of the dendrogram
    plot_dendrogram(model, truncate_mode="level", p=3)
    plt.xlabel("Number of points in node (or index of point if no parenthesis).")
    plt.show()


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

   **Total running time of the script:** (0 minutes 0.108 seconds)


.. _sphx_glr_download_auto_examples_cluster_plot_agglomerative_dendrogram.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_agglomerative_dendrogram.ipynb
        :alt: Launch binder
        :width: 150 px

    .. container:: lite-badge

      .. image:: images/jupyterlite_badge_logo.svg
        :target: ../../lite/lab/?path=auto_examples/cluster/plot_agglomerative_dendrogram.ipynb
        :alt: Launch JupyterLite
        :width: 150 px

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

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

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

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


.. include:: plot_agglomerative_dendrogram.recommendations


.. only:: html

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

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