.. only:: html

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

        Click :ref:`here <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 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.



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






.. code-block:: default


    import numpy as np

    from matplotlib import pyplot as plt
    from scipy.cluster.hierarchy import dendrogram
    from sklearn.datasets import load_iris
    from sklearn.cluster import AgglomerativeClustering


    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  0.085 seconds)


.. _sphx_glr_download_auto_examples_cluster_plot_agglomerative_dendrogram.py:


.. only :: html

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


  .. container:: binder-badge

    .. image:: https://mybinder.org/badge_logo.svg
      :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/0.23.X?urlpath=lab/tree/notebooks/auto_examples/cluster/plot_agglomerative_dendrogram.ipynb
      :width: 150 px


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

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



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

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


.. only:: html

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

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