.. _example_cluster_plot_ward_structured_vs_unstructured.py:


===========================================================
Hierarchical clustering: structured vs unstructured ward
===========================================================

Example builds a swiss roll dataset and runs
:ref:`hierarchical_clustering` on their position.

In a first step, the hierarchical clustering without connectivity
constraints on structure, solely based on distance, whereas in a second
step clustering restricted to the k-Nearest Neighbors graph: it's a
hierarchical clustering with structure prior.

Some of the clusters learned without connectivity constraints do not
respect the structure of the swiss roll and extend across different folds of
the manifolds. On the opposite, when opposing connectivity constraints,
the clusters form a nice parcellation of the swiss roll.



.. rst-class:: horizontal


    *

      .. image:: images/plot_ward_structured_vs_unstructured_2.png
            :scale: 50

    *

      .. image:: images/plot_ward_structured_vs_unstructured_1.png
            :scale: 50


**Script output**::

  Compute unstructured hierarchical clustering...
  Elapsed time:  1.67695522308
  Number of points:  1000
  Compute structured hierarchical clustering...
  Elapsed time:  0.172141075134
  Number of points:  1000



**Python source code:** :download:`plot_ward_structured_vs_unstructured.py <plot_ward_structured_vs_unstructured.py>`

.. literalinclude:: plot_ward_structured_vs_unstructured.py
    :lines: 19-