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_sources/auto_examples/plot_iris_example.rst.txt

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.. rst-class:: sphx-glr-timing
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**Total running time of the script:** (0 minutes 0.486 seconds)
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.. _sphx_glr_download_auto_examples_plot_iris_example.py:

_sources/auto_examples/sg_execution_times.rst.txt

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Computation times
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=================
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.. container::
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* - :ref:`sphx_glr_auto_examples_plot_iris_example.py` (``plot_iris_example.py``)
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_sources/sg_execution_times.rst.txt

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Computation times
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=================
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* - :ref:`sphx_glr_auto_examples_plot_iris_example.py` (``../../examples/plot_iris_example.py``)
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auto_examples/plot_iris_example.html

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We visualized the clustering results and the difference between the two clustering algorithms.
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We saw that Radius Clustering can lead to smaller clusters than kmeans, which produces much more equilibrate clusters.
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The difference plot can be very useful to see where the two clustering algorithms differ.</p>
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.461 seconds)</p>
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.486 seconds)</p>
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<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-iris-example-py">
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<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
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<p><a class="reference download internal" download="" href="../_downloads/b4cf190b888ee49e92905caa55256121/plot_iris_example.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_iris_example.ipynb</span></code></a></p>

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