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<ul>
<li><a class="reference internal" href="#"><code class="docutils literal"><span class="pre">sklearn.cluster</span></code>.AgglomerativeClustering</a><ul>
<li><a class="reference internal" href="#examples-using-sklearn-cluster-agglomerativeclustering">Examples using <code class="docutils literal"><span class="pre">sklearn.cluster.AgglomerativeClustering</span></code></a></li>
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<div class="section" id="sklearn-cluster-agglomerativeclustering">
<h1><a class="reference internal" href="../classes.html#module-sklearn.cluster" title="sklearn.cluster"><code class="xref py py-mod docutils literal"><span class="pre">sklearn.cluster</span></code></a>.AgglomerativeClustering<a class="headerlink" href="#sklearn-cluster-agglomerativeclustering" title="Permalink to this headline">¶</a></h1>
<dl class="class">
<dt id="sklearn.cluster.AgglomerativeClustering">
<em class="property">class </em><code class="descclassname">sklearn.cluster.</code><code class="descname">AgglomerativeClustering</code><span class="sig-paren">(</span><em>n_clusters=2</em>, <em>affinity='euclidean'</em>, <em>memory=None</em>, <em>connectivity=None</em>, <em>compute_full_tree='auto'</em>, <em>linkage='ward'</em>, <em>pooling_func='deprecated'</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/2d7cd0c/sklearn/cluster/hierarchical.py#L656"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.AgglomerativeClustering" title="Permalink to this definition">¶</a></dt>
<dd><p>Agglomerative Clustering</p>
<p>Recursively merges the pair of clusters that minimally increases
a given linkage distance.</p>
<p>Read more in the <a class="reference internal" href="../clustering.html#hierarchical-clustering"><span class="std std-ref">User Guide</span></a>.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><p class="first"><strong>n_clusters</strong> : int, default=2</p>
<blockquote>
<div><p>The number of clusters to find.</p>
</div></blockquote>
<p><strong>affinity</strong> : string or callable, default: “euclidean”</p>
<blockquote>
<div><p>Metric used to compute the linkage. Can be “euclidean”, “l1”, “l2”,
“manhattan”, “cosine”, or ‘precomputed’.
If linkage is “ward”, only “euclidean” is accepted.</p>
</div></blockquote>
<p><strong>memory</strong> : None, str or object with the joblib.Memory interface, optional</p>
<blockquote>
<div><p>Used to cache the output of the computation of the tree.
By default, no caching is done. If a string is given, it is the
path to the caching directory.</p>
</div></blockquote>
<p><strong>connectivity</strong> : array-like or callable, optional</p>
<blockquote>
<div><p>Connectivity matrix. Defines for each sample the neighboring
samples following a given structure of the data.
This can be a connectivity matrix itself or a callable that transforms
the data into a connectivity matrix, such as derived from
kneighbors_graph. Default is None, i.e, the
hierarchical clustering algorithm is unstructured.</p>
</div></blockquote>
<p><strong>compute_full_tree</strong> : bool or ‘auto’ (optional)</p>
<blockquote>
<div><p>Stop early the construction of the tree at n_clusters. This is
useful to decrease computation time if the number of clusters is
not small compared to the number of samples. This option is
useful only when specifying a connectivity matrix. Note also that
when varying the number of clusters and using caching, it may
be advantageous to compute the full tree.</p>
</div></blockquote>
<p><strong>linkage</strong> : {“ward”, “complete”, “average”, “single”}, optional (default=”ward”)</p>
<blockquote>
<div><p>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.</p>
<ul class="simple">
<li>ward minimizes the variance of the clusters being merged.</li>
<li>average uses the average of the distances of each observation of
the two sets.</li>
<li>complete or maximum linkage uses the maximum distances between
all observations of the two sets.</li>
<li>single uses the minimum of the distances between all observations
of the two sets.</li>
</ul>
</div></blockquote>
<p><strong>pooling_func</strong> : callable, default=’deprecated’</p>
<blockquote class="last">
<div><p>Ignored.</p>
<div class="deprecated">
<p><span class="versionmodified">Deprecated since version 0.20: </span><code class="docutils literal"><span class="pre">pooling_func</span></code> has been deprecated in 0.20 and will be removed
in 0.22.</p>
</div>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
<p class="rubric">Attributes</p>
<table border="1" class="docutils">
<colgroup>
<col width="4%" />
<col width="96%" />
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><strong>labels_</strong></td>
<td>(array [n_samples]) cluster labels for each point</td>
</tr>
<tr class="row-even"><td><strong>n_leaves_</strong></td>
<td>(int) Number of leaves in the hierarchical tree.</td>
</tr>
<tr class="row-odd"><td><strong>n_components_</strong></td>
<td>(int) The estimated number of connected components in the graph.</td>
</tr>
<tr class="row-even"><td><strong>children_</strong></td>
<td>(array-like, shape (n_samples-1, 2)) The children of each non-leaf node. Values less than <cite>n_samples</cite> correspond to leaves of the tree which are the original samples. A node <cite>i</cite> greater than or equal to <cite>n_samples</cite> is a non-leaf node and has children <cite>children_[i - n_samples]</cite>. Alternatively at the i-th iteration, children[i][0] and children[i][1] are merged to form node <cite>n_samples + i</cite></td>
</tr>
</tbody>
</table>
<p class="rubric">Methods</p>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%" />
<col width="90%" />
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#sklearn.cluster.AgglomerativeClustering.fit" title="sklearn.cluster.AgglomerativeClustering.fit"><code class="xref py py-obj docutils literal"><span class="pre">fit</span></code></a>(X[, y])</td>
<td>Fit the hierarchical clustering on the data</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="#sklearn.cluster.AgglomerativeClustering.fit_predict" title="sklearn.cluster.AgglomerativeClustering.fit_predict"><code class="xref py py-obj docutils literal"><span class="pre">fit_predict</span></code></a>(X[, y])</td>
<td>Performs clustering on X and returns cluster labels.</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="#sklearn.cluster.AgglomerativeClustering.get_params" title="sklearn.cluster.AgglomerativeClustering.get_params"><code class="xref py py-obj docutils literal"><span class="pre">get_params</span></code></a>([deep])</td>
<td>Get parameters for this estimator.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="#sklearn.cluster.AgglomerativeClustering.set_params" title="sklearn.cluster.AgglomerativeClustering.set_params"><code class="xref py py-obj docutils literal"><span class="pre">set_params</span></code></a>(**params)</td>
<td>Set the parameters of this estimator.</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="sklearn.cluster.AgglomerativeClustering.__init__">
<code class="descname">__init__</code><span class="sig-paren">(</span><em>n_clusters=2</em>, <em>affinity='euclidean'</em>, <em>memory=None</em>, <em>connectivity=None</em>, <em>compute_full_tree='auto'</em>, <em>linkage='ward'</em>, <em>pooling_func='deprecated'</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/2d7cd0c/sklearn/cluster/hierarchical.py#L738"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.AgglomerativeClustering.__init__" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="sklearn.cluster.AgglomerativeClustering.fit">
<code class="descname">fit</code><span class="sig-paren">(</span><em>X</em>, <em>y=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/2d7cd0c/sklearn/cluster/hierarchical.py#L750"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.AgglomerativeClustering.fit" title="Permalink to this definition">¶</a></dt>
<dd><p>Fit the hierarchical clustering on the data</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><p class="first"><strong>X</strong> : array-like, shape = [n_samples, n_features]</p>
<blockquote>
<div><p>Training data. Shape [n_samples, n_features], or [n_samples,
n_samples] if affinity==’precomputed’.</p>
</div></blockquote>
<p><strong>y</strong> : Ignored</p>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">self</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="sklearn.cluster.AgglomerativeClustering.fit_predict">
<code class="descname">fit_predict</code><span class="sig-paren">(</span><em>X</em>, <em>y=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/2d7cd0c/sklearn/base.py#L384"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.AgglomerativeClustering.fit_predict" title="Permalink to this definition">¶</a></dt>
<dd><p>Performs clustering on X and returns cluster labels.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><p class="first"><strong>X</strong> : ndarray, shape (n_samples, n_features)</p>
<blockquote>
<div><p>Input data.</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>y</strong> : ndarray, shape (n_samples,)</p>
<blockquote class="last">
<div><p>cluster labels</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="sklearn.cluster.AgglomerativeClustering.get_params">
<code class="descname">get_params</code><span class="sig-paren">(</span><em>deep=True</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/2d7cd0c/sklearn/base.py#L213"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.AgglomerativeClustering.get_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Get parameters for this estimator.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><p class="first"><strong>deep</strong> : boolean, optional</p>
<blockquote>
<div><p>If True, will return the parameters for this estimator and
contained subobjects that are estimators.</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>params</strong> : mapping of string to any</p>
<blockquote class="last">
<div><p>Parameter names mapped to their values.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="sklearn.cluster.AgglomerativeClustering.set_params">
<code class="descname">set_params</code><span class="sig-paren">(</span><em>**params</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/2d7cd0c/sklearn/base.py#L236"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.AgglomerativeClustering.set_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Set the parameters of this estimator.</p>
<p>The method works on simple estimators as well as on nested objects
(such as pipelines). The latter have parameters of the form
<code class="docutils literal"><span class="pre"><component>__<parameter></span></code> so that it’s possible to update each
component of a nested object.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">self</td>
</tr>
</tbody>
</table>
</dd></dl>
</dd></dl>
<div class="section" id="examples-using-sklearn-cluster-agglomerativeclustering">
<h2>Examples using <code class="docutils literal"><span class="pre">sklearn.cluster.AgglomerativeClustering</span></code><a class="headerlink" href="#examples-using-sklearn-cluster-agglomerativeclustering" title="Permalink to this headline">¶</a></h2>
<div class="sphx-glr-thumbcontainer" tooltip="Compute the segmentation of a 2D image with Ward hierarchical clustering. The clustering is spa..."><div class="figure" id="id1">
<img alt="../../_images/sphx_glr_plot_coin_ward_segmentation_thumb.png" src="../../_images/sphx_glr_plot_coin_ward_segmentation_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/cluster/plot_coin_ward_segmentation.html#sphx-glr-auto-examples-cluster-plot-coin-ward-segmentation-py"><span class="std std-ref">A demo of structured Ward hierarchical clustering on an image of coins</span></a></span></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows the effect of imposing a connectivity graph to capture local structure in th..."><div class="figure" id="id2">
<img alt="../../_images/sphx_glr_plot_agglomerative_clustering_thumb.png" src="../../_images/sphx_glr_plot_agglomerative_clustering_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/cluster/plot_agglomerative_clustering.html#sphx-glr-auto-examples-cluster-plot-agglomerative-clustering-py"><span class="std std-ref">Agglomerative clustering with and without structure</span></a></span></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="An illustration of various linkage option for agglomerative clustering on a 2D embedding of the..."><div class="figure" id="id3">
<img alt="../../_images/sphx_glr_plot_digits_linkage_thumb.png" src="../../_images/sphx_glr_plot_digits_linkage_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/cluster/plot_digits_linkage.html#sphx-glr-auto-examples-cluster-plot-digits-linkage-py"><span class="std std-ref">Various Agglomerative Clustering on a 2D embedding of digits</span></a></span></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Example builds a swiss roll dataset and runs hierarchical clustering on their position."><div class="figure" id="id4">
<img alt="../../_images/sphx_glr_plot_ward_structured_vs_unstructured_thumb.png" src="../../_images/sphx_glr_plot_ward_structured_vs_unstructured_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/cluster/plot_ward_structured_vs_unstructured.html#sphx-glr-auto-examples-cluster-plot-ward-structured-vs-unstructured-py"><span class="std std-ref">Hierarchical clustering: structured vs unstructured ward</span></a></span></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Demonstrates the effect of different metrics on the hierarchical clustering."><div class="figure" id="id5">
<img alt="../../_images/sphx_glr_plot_agglomerative_clustering_metrics_thumb.png" src="../../_images/sphx_glr_plot_agglomerative_clustering_metrics_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/cluster/plot_agglomerative_clustering_metrics.html#sphx-glr-auto-examples-cluster-plot-agglomerative-clustering-metrics-py"><span class="std std-ref">Agglomerative clustering with different metrics</span></a></span></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows characteristics of different linkage methods for hierarchical clustering on ..."><div class="figure" id="id6">
<img alt="../../_images/sphx_glr_plot_linkage_comparison_thumb.png" src="../../_images/sphx_glr_plot_linkage_comparison_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/cluster/plot_linkage_comparison.html#sphx-glr-auto-examples-cluster-plot-linkage-comparison-py"><span class="std std-ref">Comparing different hierarchical linkage methods on toy datasets</span></a></span></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows characteristics of different clustering algorithms on datasets that are "int..."><div class="figure" id="id7">
<img alt="../../_images/sphx_glr_plot_cluster_comparison_thumb.png" src="../../_images/sphx_glr_plot_cluster_comparison_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/cluster/plot_cluster_comparison.html#sphx-glr-auto-examples-cluster-plot-cluster-comparison-py"><span class="std std-ref">Comparing different clustering algorithms on toy datasets</span></a></span></p>
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