.. _`Agglomerative Clustering`:

.. _`com.sympathyfordata.advancedmachinelearning.agglomerativeclustering`:

Agglomerative Clustering
````````````````````````

.. image:: agglomerative_clustering.svg
   :width: 48


Recursively merges the pair of clusters that minimally increases a given linkage distance.




Documentation
:::::::::::::

Attributes
==========

    **children_**
        The children of each non-leaf node. Values less than `n_samples`
        correspond to leaves of the tree which are the original samples.
        A node `i` greater than or equal to `n_samples` is a non-leaf
        node and has children `children_[i - n_samples]`. Alternatively
        at the i-th iteration, children[i] and children[i]
        are merged to form node `n_samples + i`.


    **labels_**
        Cluster labels for each point.


    **n_components**


    **n_leaves_**
        Number of leaves in the hierarchical tree.


Definition
::::::::::


Output ports
============

    **model**  model
        Model




Configuration
=============

    **Affinity** (affinity)
        (no description)
    **linkage** (linkage)
        Which linkage criterion to use. The linkage criterion determines which
        distance to use between sets of observation. The algorithm will merge
        the pairs of cluster that minimize this criterion.

        - 'ward' minimizes the variance of the clusters being merged.
        - 'average' uses the average of the distances of each observation of
          the two sets.
        - 'complete' or 'maximum' linkage uses the maximum distances between
          all observations of the two sets.
        - 'single' uses the minimum of the distances between all observations
          of the two sets.

        .. versionadded:: 0.20
            Added the 'single' option

        For examples comparing different `linkage` criteria, see
        sphx_glr_auto_examples_cluster_plot_linkage_comparison.py.
    **Number of clusters** (n_clusters)
        The number of clusters to find. It must be ``None`` if
        ``distance_threshold`` is not ``None``.







Examples
========

* :download:`agglomerative_clustering.syx </Examples/Advanced Machine Learning/agglomerative_clustering.syx>`




Implementation
==============

.. automodule:: node_clustering2
    :noindex:

.. class:: AgglomerativeClustering
    :noindex: