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<ul>
<li><a class="reference internal" href="#"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.cluster</span></code>.AgglomerativeClustering</a><ul>
<li><a class="reference internal" href="#sklearn.cluster.AgglomerativeClustering"><code class="docutils literal notranslate"><span class="pre">AgglomerativeClustering</span></code></a><ul>
<li><a class="reference internal" href="#sklearn.cluster.AgglomerativeClustering.fit"><code class="docutils literal notranslate"><span class="pre">AgglomerativeClustering.fit</span></code></a></li>
<li><a class="reference internal" href="#sklearn.cluster.AgglomerativeClustering.fit_predict"><code class="docutils literal notranslate"><span class="pre">AgglomerativeClustering.fit_predict</span></code></a></li>
<li><a class="reference internal" href="#sklearn.cluster.AgglomerativeClustering.get_metadata_routing"><code class="docutils literal notranslate"><span class="pre">AgglomerativeClustering.get_metadata_routing</span></code></a></li>
<li><a class="reference internal" href="#sklearn.cluster.AgglomerativeClustering.get_params"><code class="docutils literal notranslate"><span class="pre">AgglomerativeClustering.get_params</span></code></a></li>
<li><a class="reference internal" href="#sklearn.cluster.AgglomerativeClustering.set_params"><code class="docutils literal notranslate"><span class="pre">AgglomerativeClustering.set_params</span></code></a></li>
</ul>
</li>
<li><a class="reference internal" href="#examples-using-sklearn-cluster-agglomerativeclustering">Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.cluster.AgglomerativeClustering</span></code></a></li>
</ul>
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<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 notranslate"><span class="pre">sklearn.cluster</span></code></a>.AgglomerativeClustering<a class="headerlink" href="#sklearn-cluster-agglomerativeclustering" title="Permalink to this heading">¶</a></h1>
<dl class="py class">
<dt class="sig sig-object py" id="sklearn.cluster.AgglomerativeClustering">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.cluster.</span></span><span class="sig-name descname"><span class="pre">AgglomerativeClustering</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_clusters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affinity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">connectivity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compute_full_tree</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">linkage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'ward'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">distance_threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compute_distances</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/3f89022fa/sklearn/cluster/_agglomerative.py#L763"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.AgglomerativeClustering" title="Permalink to this definition">¶</a></dt>
<dd><p>Agglomerative Clustering.</p>
<p>Recursively merges pair of clusters of sample data; uses 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>
<dl class="field-list">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl>
<dt><strong>n_clusters</strong><span class="classifier">int or None, default=2</span></dt><dd><p>The number of clusters to find. It must be <code class="docutils literal notranslate"><span class="pre">None</span></code> if
<code class="docutils literal notranslate"><span class="pre">distance_threshold</span></code> is not <code class="docutils literal notranslate"><span class="pre">None</span></code>.</p>
</dd>
<dt><strong>affinity</strong><span class="classifier">str or callable, default=’euclidean’</span></dt><dd><p>The metric to use when calculating distance between instances in a
feature array. If metric is a string or callable, it must be one of
the options allowed by <a class="reference internal" href="sklearn.metrics.pairwise_distances.html#sklearn.metrics.pairwise_distances" title="sklearn.metrics.pairwise_distances"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.metrics.pairwise_distances</span></code></a> for
its metric parameter.
If linkage is “ward”, only “euclidean” is accepted.
If “precomputed”, a distance matrix (instead of a similarity matrix)
is needed as input for the fit method.</p>
<div class="deprecated">
<p><span class="versionmodified deprecated">Deprecated since version 1.2: </span><code class="docutils literal notranslate"><span class="pre">affinity</span></code> was deprecated in version 1.2 and will be renamed to
<code class="docutils literal notranslate"><span class="pre">metric</span></code> in 1.4.</p>
</div>
</dd>
<dt><strong>metric</strong><span class="classifier">str or callable, default=None</span></dt><dd><p>Metric used to compute the linkage. Can be “euclidean”, “l1”, “l2”,
“manhattan”, “cosine”, or “precomputed”. If set to <code class="docutils literal notranslate"><span class="pre">None</span></code> then
“euclidean” is used. If linkage is “ward”, only “euclidean” is
accepted. If “precomputed”, a distance matrix is needed as input for
the fit method.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 1.2.</span></p>
</div>
</dd>
<dt><strong>memory</strong><span class="classifier">str or object with the joblib.Memory interface, default=None</span></dt><dd><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>
</dd>
<dt><strong>connectivity</strong><span class="classifier">array-like or callable, default=None</span></dt><dd><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
<code class="docutils literal notranslate"><span class="pre">kneighbors_graph</span></code>. Default is <code class="docutils literal notranslate"><span class="pre">None</span></code>, i.e, the
hierarchical clustering algorithm is unstructured.</p>
</dd>
<dt><strong>compute_full_tree</strong><span class="classifier">‘auto’ or bool, default=’auto’</span></dt><dd><p>Stop early the construction of the tree at <code class="docutils literal notranslate"><span class="pre">n_clusters</span></code>. 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. It must be <code class="docutils literal notranslate"><span class="pre">True</span></code> if <code class="docutils literal notranslate"><span class="pre">distance_threshold</span></code> is not
<code class="docutils literal notranslate"><span class="pre">None</span></code>. By default <code class="docutils literal notranslate"><span class="pre">compute_full_tree</span></code> is “auto”, which is equivalent
to <code class="docutils literal notranslate"><span class="pre">True</span></code> when <code class="docutils literal notranslate"><span class="pre">distance_threshold</span></code> is not <code class="docutils literal notranslate"><span class="pre">None</span></code> or that <code class="docutils literal notranslate"><span class="pre">n_clusters</span></code>
is inferior to the maximum between 100 or <code class="docutils literal notranslate"><span class="pre">0.02</span> <span class="pre">*</span> <span class="pre">n_samples</span></code>.
Otherwise, “auto” is equivalent to <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p>
</dd>
<dt><strong>linkage</strong><span class="classifier">{‘ward’, ‘complete’, ‘average’, ‘single’}, default=’ward’</span></dt><dd><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><p>‘ward’ minimizes the variance of the clusters being merged.</p></li>
<li><p>‘average’ uses the average of the distances of each observation of
the two sets.</p></li>
<li><p>‘complete’ or ‘maximum’ linkage uses the maximum distances between
all observations of the two sets.</p></li>
<li><p>‘single’ uses the minimum of the distances between all observations
of the two sets.</p></li>
</ul>
<div class="versionadded">
<p><span class="versionmodified added">New in version 0.20: </span>Added the ‘single’ option</p>
</div>
</dd>
<dt><strong>distance_threshold</strong><span class="classifier">float, default=None</span></dt><dd><p>The linkage distance threshold at or above which clusters will not be
merged. If not <code class="docutils literal notranslate"><span class="pre">None</span></code>, <code class="docutils literal notranslate"><span class="pre">n_clusters</span></code> must be <code class="docutils literal notranslate"><span class="pre">None</span></code> and
<code class="docutils literal notranslate"><span class="pre">compute_full_tree</span></code> must be <code class="docutils literal notranslate"><span class="pre">True</span></code>.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 0.21.</span></p>
</div>
</dd>
<dt><strong>compute_distances</strong><span class="classifier">bool, default=False</span></dt><dd><p>Computes distances between clusters even if <code class="docutils literal notranslate"><span class="pre">distance_threshold</span></code> is not
used. This can be used to make dendrogram visualization, but introduces
a computational and memory overhead.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 0.24.</span></p>
</div>
</dd>
</dl>
</dd>
<dt class="field-even">Attributes<span class="colon">:</span></dt>
<dd class="field-even"><dl>
<dt><strong>n_clusters_</strong><span class="classifier">int</span></dt><dd><p>The number of clusters found by the algorithm. If
<code class="docutils literal notranslate"><span class="pre">distance_threshold=None</span></code>, it will be equal to the given
<code class="docutils literal notranslate"><span class="pre">n_clusters</span></code>.</p>
</dd>
<dt><strong>labels_</strong><span class="classifier">ndarray of shape (n_samples)</span></dt><dd><p>Cluster labels for each point.</p>
</dd>
<dt><strong>n_leaves_</strong><span class="classifier">int</span></dt><dd><p>Number of leaves in the hierarchical tree.</p>
</dd>
<dt><strong>n_connected_components_</strong><span class="classifier">int</span></dt><dd><p>The estimated number of connected components in the graph.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 0.21: </span><code class="docutils literal notranslate"><span class="pre">n_connected_components_</span></code> was added to replace <code class="docutils literal notranslate"><span class="pre">n_components_</span></code>.</p>
</div>
</dd>
<dt><strong>n_features_in_</strong><span class="classifier">int</span></dt><dd><p>Number of features seen during <a class="reference internal" href="../../glossary.html#term-fit"><span class="xref std std-term">fit</span></a>.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 0.24.</span></p>
</div>
</dd>
<dt><strong>feature_names_in_</strong><span class="classifier">ndarray of shape (<code class="docutils literal notranslate"><span class="pre">n_features_in_</span></code>,)</span></dt><dd><p>Names of features seen during <a class="reference internal" href="../../glossary.html#term-fit"><span class="xref std std-term">fit</span></a>. Defined only when <code class="docutils literal notranslate"><span class="pre">X</span></code>
has feature names that are all strings.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 1.0.</span></p>
</div>
</dd>
<dt><strong>children_</strong><span class="classifier">array-like of shape (n_samples-1, 2)</span></dt><dd><p>The children of each non-leaf node. Values less than <code class="docutils literal notranslate"><span class="pre">n_samples</span></code>
correspond to leaves of the tree which are the original samples.
A node <code class="docutils literal notranslate"><span class="pre">i</span></code> greater than or equal to <code class="docutils literal notranslate"><span class="pre">n_samples</span></code> is a non-leaf
node and has children <code class="docutils literal notranslate"><span class="pre">children_[i</span> <span class="pre">-</span> <span class="pre">n_samples]</span></code>. Alternatively
at the i-th iteration, children[i][0] and children[i][1]
are merged to form node <code class="docutils literal notranslate"><span class="pre">n_samples</span> <span class="pre">+</span> <span class="pre">i</span></code>.</p>
</dd>
<dt><strong>distances_</strong><span class="classifier">array-like of shape (n_nodes-1,)</span></dt><dd><p>Distances between nodes in the corresponding place in <code class="docutils literal notranslate"><span class="pre">children_</span></code>.
Only computed if <code class="docutils literal notranslate"><span class="pre">distance_threshold</span></code> is used or <code class="docutils literal notranslate"><span class="pre">compute_distances</span></code>
is set to <code class="docutils literal notranslate"><span class="pre">True</span></code>.</p>
</dd>
</dl>
</dd>
</dl>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<dl class="simple">
<dt><a class="reference internal" href="sklearn.cluster.FeatureAgglomeration.html#sklearn.cluster.FeatureAgglomeration" title="sklearn.cluster.FeatureAgglomeration"><code class="xref py py-obj docutils literal notranslate"><span class="pre">FeatureAgglomeration</span></code></a></dt><dd><p>Agglomerative clustering but for features instead of samples.</p>
</dd>
<dt><a class="reference internal" href="sklearn.cluster.ward_tree.html#sklearn.cluster.ward_tree" title="sklearn.cluster.ward_tree"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ward_tree</span></code></a></dt><dd><p>Hierarchical clustering with ward linkage.</p>
</dd>
</dl>
</div>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="kn">import</span> <span class="n">AgglomerativeClustering</span>
<span class="gp">>>> </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="gp">>>> </span><span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
<span class="gp">... </span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">0</span><span class="p">]])</span>
<span class="gp">>>> </span><span class="n">clustering</span> <span class="o">=</span> <span class="n">AgglomerativeClustering</span><span class="p">()</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">clustering</span>
<span class="go">AgglomerativeClustering()</span>
<span class="gp">>>> </span><span class="n">clustering</span><span class="o">.</span><span class="n">labels_</span>
<span class="go">array([1, 1, 1, 0, 0, 0])</span>
</pre></div>
</div>
<p class="rubric">Methods</p>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.cluster.AgglomerativeClustering.fit" title="sklearn.cluster.AgglomerativeClustering.fit"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit</span></code></a>(X[, y])</p></td>
<td><p>Fit the hierarchical clustering from features, or distance matrix.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.cluster.AgglomerativeClustering.fit_predict" title="sklearn.cluster.AgglomerativeClustering.fit_predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit_predict</span></code></a>(X[, y])</p></td>
<td><p>Fit and return the result of each sample's clustering assignment.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.cluster.AgglomerativeClustering.get_metadata_routing" title="sklearn.cluster.AgglomerativeClustering.get_metadata_routing"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_metadata_routing</span></code></a>()</p></td>
<td><p>Get metadata routing of this object.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.cluster.AgglomerativeClustering.get_params" title="sklearn.cluster.AgglomerativeClustering.get_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_params</span></code></a>([deep])</p></td>
<td><p>Get parameters for this estimator.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.cluster.AgglomerativeClustering.set_params" title="sklearn.cluster.AgglomerativeClustering.set_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_params</span></code></a>(**params)</p></td>
<td><p>Set the parameters of this estimator.</p></td>
</tr>
</tbody>
</table>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.cluster.AgglomerativeClustering.fit">
<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/3f89022fa/sklearn/cluster/_agglomerative.py#L959"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.AgglomerativeClustering.fit" title="Permalink to this definition">¶</a></dt>
<dd><p>Fit the hierarchical clustering from features, or distance matrix.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like, shape (n_samples, n_features) or (n_samples, n_samples)</span></dt><dd><p>Training instances to cluster, or distances between instances if
<code class="docutils literal notranslate"><span class="pre">metric='precomputed'</span></code>.</p>
</dd>
<dt><strong>y</strong><span class="classifier">Ignored</span></dt><dd><p>Not used, present here for API consistency by convention.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">object</span></dt><dd><p>Returns the fitted instance.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.cluster.AgglomerativeClustering.fit_predict">
<span class="sig-name descname"><span class="pre">fit_predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/3f89022fa/sklearn/cluster/_agglomerative.py#L1103"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.AgglomerativeClustering.fit_predict" title="Permalink to this definition">¶</a></dt>
<dd><p>Fit and return the result of each sample’s clustering assignment.</p>
<p>In addition to fitting, this method also return the result of the
clustering assignment for each sample in the training set.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like of shape (n_samples, n_features) or (n_samples, n_samples)</span></dt><dd><p>Training instances to cluster, or distances between instances if
<code class="docutils literal notranslate"><span class="pre">affinity='precomputed'</span></code>.</p>
</dd>
<dt><strong>y</strong><span class="classifier">Ignored</span></dt><dd><p>Not used, present here for API consistency by convention.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>labels</strong><span class="classifier">ndarray of shape (n_samples,)</span></dt><dd><p>Cluster labels.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.cluster.AgglomerativeClustering.get_metadata_routing">
<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/3f89022fa/sklearn/utils/_metadata_requests.py#L1243"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.AgglomerativeClustering.get_metadata_routing" title="Permalink to this definition">¶</a></dt>
<dd><p>Get metadata routing of this object.</p>
<p>Please check <a class="reference internal" href="../../metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
mechanism works.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>routing</strong><span class="classifier">MetadataRequest</span></dt><dd><p>A <a class="reference internal" href="sklearn.utils.metadata_routing.MetadataRequest.html#sklearn.utils.metadata_routing.MetadataRequest" title="sklearn.utils.metadata_routing.MetadataRequest"><code class="xref py py-class docutils literal notranslate"><span class="pre">MetadataRequest</span></code></a> encapsulating
routing information.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.cluster.AgglomerativeClustering.get_params">
<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/3f89022fa/sklearn/base.py#L178"><span class="viewcode-link"><span class="pre">[source]</span></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>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>deep</strong><span class="classifier">bool, default=True</span></dt><dd><p>If True, will return the parameters for this estimator and
contained subobjects that are estimators.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>params</strong><span class="classifier">dict</span></dt><dd><p>Parameter names mapped to their values.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.cluster.AgglomerativeClustering.set_params">
<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/3f89022fa/sklearn/base.py#L202"><span class="viewcode-link"><span class="pre">[source]</span></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 <a class="reference internal" href="sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
parameters of the form <code class="docutils literal notranslate"><span class="pre"><component>__<parameter></span></code> so that it’s
possible to update each component of a nested object.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>**params</strong><span class="classifier">dict</span></dt><dd><p>Estimator parameters.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">estimator instance</span></dt><dd><p>Estimator instance.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
</dd></dl>
<section id="examples-using-sklearn-cluster-agglomerativeclustering">
<h2>Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.cluster.AgglomerativeClustering</span></code><a class="headerlink" href="#examples-using-sklearn-cluster-agglomerativeclustering" title="Permalink to this heading">¶</a></h2>
<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="Compute the segmentation of a 2D image with Ward hierarchical clustering. The clustering is spa..."><img alt="" src="../../_images/sphx_glr_plot_coin_ward_segmentation_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">A demo of structured Ward hierarchical clustering on an image of coins</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows the effect of imposing a connectivity graph to capture local structure in th..."><img alt="" src="../../_images/sphx_glr_plot_agglomerative_clustering_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Agglomerative clustering with and without structure</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Demonstrates the effect of different metrics on the hierarchical clustering."><img alt="" src="../../_images/sphx_glr_plot_agglomerative_clustering_metrics_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Agglomerative clustering with different metrics</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows characteristics of different clustering algorithms on datasets that are "int..."><img alt="" src="../../_images/sphx_glr_plot_cluster_comparison_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Comparing different clustering algorithms on toy datasets</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows characteristics of different linkage methods for hierarchical clustering on ..."><img alt="" src="../../_images/sphx_glr_plot_linkage_comparison_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Comparing different hierarchical linkage methods on toy datasets</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Example builds a swiss roll dataset and runs hierarchical clustering on their position."><img alt="" src="../../_images/sphx_glr_plot_ward_structured_vs_unstructured_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Hierarchical clustering: structured vs unstructured ward</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Clustering can be expensive, especially when our dataset contains millions of datapoints. Many ..."><img alt="" src="../../_images/sphx_glr_plot_inductive_clustering_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/cluster/plot_inductive_clustering.html#sphx-glr-auto-examples-cluster-plot-inductive-clustering-py"><span class="std std-ref">Inductive Clustering</span></a></p>
<div class="sphx-glr-thumbnail-title">Inductive Clustering</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Plot Hierarchical Clustering Dendrogram"><img alt="" src="../../_images/sphx_glr_plot_agglomerative_dendrogram_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/cluster/plot_agglomerative_dendrogram.html#sphx-glr-auto-examples-cluster-plot-agglomerative-dendrogram-py"><span class="std std-ref">Plot Hierarchical Clustering Dendrogram</span></a></p>
<div class="sphx-glr-thumbnail-title">Plot Hierarchical Clustering Dendrogram</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="An illustration of various linkage option for agglomerative clustering on a 2D embedding of the..."><img alt="" src="../../_images/sphx_glr_plot_digits_linkage_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Various Agglomerative Clustering on a 2D embedding of digits</div>
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