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K-means Clustering in Scikit-learn

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.

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Version

In [1]:
import sklearn
sklearn.__version__
Out[1]:
'0.18'

Imports

This tutorial imports KMeans.

In [2]:
print(__doc__)

import plotly.plotly as py
import plotly.graph_objs as go
from plotly import tools

import numpy as np

from sklearn.cluster import KMeans
from sklearn import datasets
Automatically created module for IPython interactive environment

Plots

In [3]:
np.random.seed(5)

fig = tools.make_subplots(rows=2, cols=3,
                          print_grid=False,
                          specs=[[{'is_3d': True}, {'is_3d': True}, {'is_3d': True}],
                                 [ {'is_3d': True, 'rowspan':1}, None, None]])
scene = dict(
    camera = dict(
    up=dict(x=0, y=0, z=1),
    center=dict(x=0, y=0, z=0),
    eye=dict(x=2.5, y=0.1, z=0.1)
    ),
    xaxis=dict(
        range=[-1, 4],
        title='Petal width',
        gridcolor='rgb(255, 255, 255)',
        zerolinecolor='rgb(255, 255, 255)',
        showbackground=True,
        backgroundcolor='rgb(230, 230,230)',
        showticklabels=False, ticks=''
    ),
    yaxis=dict(
        range=[4, 8],
        title='Sepal length',
        gridcolor='rgb(255, 255, 255)',
        zerolinecolor='rgb(255, 255, 255)',
        showbackground=True,
        backgroundcolor='rgb(230, 230,230)',
        showticklabels=False, ticks=''
    ),
    zaxis=dict(
        range=[1,8],
        title='Petal length',
        gridcolor='rgb(255, 255, 255)',
        zerolinecolor='rgb(255, 255, 255)',
        showbackground=True,
        backgroundcolor='rgb(230, 230,230)',
        showticklabels=False, ticks=''
    )
)

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():
    est.fit(X)
    labels = est.labels_

    trace = go.Scatter3d(x=X[:, 3], y=X[:, 0], z=X[:, 2],
                         showlegend=False,
                         mode='markers',
                         marker=dict(
                                color=labels.astype(np.float),
                                line=dict(color='black', width=1)
        ))
    fig.append_trace(trace, 1, fignum)
    
    fignum = fignum + 1

y = np.choose(y, [1, 2, 0]).astype(np.float)

trace1 = go.Scatter3d(x=X[:, 3], y=X[:, 0], z=X[:, 2],
                      showlegend=False,
                      mode='markers',
                      marker=dict(
                            color=y,
                            line=dict(color='black', width=1)))
fig.append_trace(trace1, 2, 1)

fig['layout'].update(height=900, width=900,
                     margin=dict(l=10,r=10))

fig['layout']['scene1'].update(scene)
fig['layout']['scene2'].update(scene)
fig['layout']['scene3'].update(scene)
fig['layout']['scene4'].update(scene)
fig['layout']['scene5'].update(scene)
In [4]:
py.iplot(fig)
Out[4]:

License

Code source:

         Gaƫl Varoquaux

Modified for documentation by Jaques Grobler

License:

        BSD 3 clause
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