.. _sphx_glr_auto_examples_cluster_plot_cluster_iris.py:


=========================================================
K-means Clustering
=========================================================

The plots display firstly what a K-means algorithm would yield
using three clusters. It is then shown what the effect of a bad
initialization is on the classification process:
By setting n_init to only 1 (default is 10), the amount of
times that the algorithm will be run with different centroid
seeds is reduced.
The next plot displays what using eight clusters would deliver
and finally the ground truth.




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


    *

      .. image:: /auto_examples/cluster/images/sphx_glr_plot_cluster_iris_001.png
            :scale: 47

    *

      .. image:: /auto_examples/cluster/images/sphx_glr_plot_cluster_iris_002.png
            :scale: 47

    *

      .. image:: /auto_examples/cluster/images/sphx_glr_plot_cluster_iris_003.png
            :scale: 47

    *

      .. image:: /auto_examples/cluster/images/sphx_glr_plot_cluster_iris_004.png
            :scale: 47





.. code-block:: python

    print(__doc__)


    # Code source: Gaël Varoquaux
    # Modified for documentation by Jaques Grobler
    # License: BSD 3 clause

    import numpy as np
    import matplotlib.pyplot as plt
    from mpl_toolkits.mplot3d import Axes3D


    from sklearn.cluster import KMeans
    from sklearn import datasets

    np.random.seed(5)

    centers = [[1, 1], [-1, -1], [1, -1]]
    iris = datasets.load_iris()
    X = iris.data
    y = iris.target

    estimators = {'k_means_iris_3': KMeans(n_clusters=3),
                  'k_means_iris_8': KMeans(n_clusters=8),
                  'k_means_iris_bad_init': KMeans(n_clusters=3, n_init=1,
                                                  init='random')}


    fignum = 1
    for name, est in estimators.items():
        fig = plt.figure(fignum, figsize=(4, 3))
        plt.clf()
        ax = Axes3D(fig, rect=[0, 0, .95, 1], elev=48, azim=134)

        plt.cla()
        est.fit(X)
        labels = est.labels_

        ax.scatter(X[:, 3], X[:, 0], X[:, 2], c=labels.astype(np.float))

        ax.w_xaxis.set_ticklabels([])
        ax.w_yaxis.set_ticklabels([])
        ax.w_zaxis.set_ticklabels([])
        ax.set_xlabel('Petal width')
        ax.set_ylabel('Sepal length')
        ax.set_zlabel('Petal length')
        fignum = fignum + 1

    # Plot the ground truth
    fig = plt.figure(fignum, figsize=(4, 3))
    plt.clf()
    ax = Axes3D(fig, rect=[0, 0, .95, 1], elev=48, azim=134)

    plt.cla()

    for name, label in [('Setosa', 0),
                        ('Versicolour', 1),
                        ('Virginica', 2)]:
        ax.text3D(X[y == label, 3].mean(),
                  X[y == label, 0].mean() + 1.5,
                  X[y == label, 2].mean(), name,
                  horizontalalignment='center',
                  bbox=dict(alpha=.5, edgecolor='w', facecolor='w'))
    # Reorder the labels to have colors matching the cluster results
    y = np.choose(y, [1, 2, 0]).astype(np.float)
    ax.scatter(X[:, 3], X[:, 0], X[:, 2], c=y)

    ax.w_xaxis.set_ticklabels([])
    ax.w_yaxis.set_ticklabels([])
    ax.w_zaxis.set_ticklabels([])
    ax.set_xlabel('Petal width')
    ax.set_ylabel('Sepal length')
    ax.set_zlabel('Petal length')
    plt.show()

**Total running time of the script:**
(0 minutes 0.415 seconds)



.. container:: sphx-glr-download

    **Download Python source code:** :download:`plot_cluster_iris.py <plot_cluster_iris.py>`


.. container:: sphx-glr-download

    **Download IPython notebook:** :download:`plot_cluster_iris.ipynb <plot_cluster_iris.ipynb>`