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<li><a class="reference internal" href="#"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.cluster</span></code>.FeatureAgglomeration</a><ul>
<li><a class="reference internal" href="#examples-using-sklearn-cluster-featureagglomeration">Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.cluster.FeatureAgglomeration</span></code></a></li>
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<div class="section" id="sklearn-cluster-featureagglomeration">
<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>.FeatureAgglomeration<a class="headerlink" href="#sklearn-cluster-featureagglomeration" title="Permalink to this headline">¶</a></h1>
<dl class="py class">
<dt id="sklearn.cluster.FeatureAgglomeration">
<em class="property">class </em><code class="sig-prename descclassname">sklearn.cluster.</code><code class="sig-name descname">FeatureAgglomeration</code><span class="sig-paren">(</span><em class="sig-param">n_clusters=2</em>, <em class="sig-param">*</em>, <em class="sig-param">affinity='euclidean'</em>, <em class="sig-param">memory=None</em>, <em class="sig-param">connectivity=None</em>, <em class="sig-param">compute_full_tree='auto'</em>, <em class="sig-param">linkage='ward'</em>, <em class="sig-param">pooling_func=<function mean></em>, <em class="sig-param">distance_threshold=None</em>, <em class="sig-param">compute_distances=False</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/23d876161/sklearn/cluster/_agglomerative.py#L949"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.FeatureAgglomeration" title="Permalink to this definition">¶</a></dt>
<dd><p>Agglomerate features.</p>
<p>Similar to AgglomerativeClustering, but recursively merges features
instead of samples.</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</dt>
<dd class="field-odd"><dl>
<dt><strong>n_clusters</strong><span class="classifier">int, 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>Metric used to compute the linkage. Can be “euclidean”, “l1”, “l2”,
“manhattan”, “cosine”, or ‘precomputed’.
If linkage is “ward”, only “euclidean” is accepted.</p>
</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 feature the neighboring
features 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>
</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 n_clusters. This is useful
to decrease computation time if the number of clusters is not small
compared to the number of features. 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 features. 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 feature of
the two sets.</p></li>
<li><p>complete or maximum linkage uses the maximum distances between
all features 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>
</dd>
<dt><strong>pooling_func</strong><span class="classifier">callable, default=np.mean</span></dt><dd><p>This combines the values of agglomerated features into a single
value, and should accept an array of shape [M, N] and the keyword
argument <code class="docutils literal notranslate"><span class="pre">axis=1</span></code>, and reduce it to an array of size [M].</p>
</dd>
<dt><strong>distance_threshold</strong><span class="classifier">float, default=None</span></dt><dd><p>The linkage distance threshold 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</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">array-like of (n_features,)</span></dt><dd><p>cluster labels for each feature.</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>children_</strong><span class="classifier">array-like of shape (n_nodes-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_features</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_features</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_features]</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_features</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>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></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="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">datasets</span><span class="p">,</span> <span class="n">cluster</span>
<span class="gp">>>> </span><span class="n">digits</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">load_digits</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">images</span> <span class="o">=</span> <span class="n">digits</span><span class="o">.</span><span class="n">images</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">reshape</span><span class="p">(</span><span class="n">images</span><span class="p">,</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">images</span><span class="p">),</span> <span class="o">-</span><span class="mi">1</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">agglo</span> <span class="o">=</span> <span class="n">cluster</span><span class="o">.</span><span class="n">FeatureAgglomeration</span><span class="p">(</span><span class="n">n_clusters</span><span class="o">=</span><span class="mi">32</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">agglo</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="go">FeatureAgglomeration(n_clusters=32)</span>
<span class="gp">>>> </span><span class="n">X_reduced</span> <span class="o">=</span> <span class="n">agglo</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">X_reduced</span><span class="o">.</span><span class="n">shape</span>
<span class="go">(1797, 32)</span>
</pre></div>
</div>
<p class="rubric">Methods</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.cluster.FeatureAgglomeration.fit" title="sklearn.cluster.FeatureAgglomeration.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 on the data</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.cluster.FeatureAgglomeration.fit_transform" title="sklearn.cluster.FeatureAgglomeration.fit_transform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit_transform</span></code></a>(X[, y])</p></td>
<td><p>Fit to data, then transform it.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.cluster.FeatureAgglomeration.get_params" title="sklearn.cluster.FeatureAgglomeration.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-even"><td><p><a class="reference internal" href="#sklearn.cluster.FeatureAgglomeration.inverse_transform" title="sklearn.cluster.FeatureAgglomeration.inverse_transform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">inverse_transform</span></code></a>(Xred)</p></td>
<td><p>Inverse the transformation.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.cluster.FeatureAgglomeration.set_params" title="sklearn.cluster.FeatureAgglomeration.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>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.cluster.FeatureAgglomeration.transform" title="sklearn.cluster.FeatureAgglomeration.transform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">transform</span></code></a>(X)</p></td>
<td><p>Transform a new matrix using the built clustering</p></td>
</tr>
</tbody>
</table>
<dl class="py method">
<dt id="sklearn.cluster.FeatureAgglomeration.fit">
<code class="sig-name descname">fit</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">X</span></em>, <em class="sig-param"><span class="n">y</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">params</span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/23d876161/sklearn/cluster/_agglomerative.py#L1084"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.FeatureAgglomeration.fit" title="Permalink to this definition">¶</a></dt>
<dd><p>Fit the hierarchical clustering on the data</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like of shape (n_samples, n_features)</span></dt><dd><p>The data</p>
</dd>
<dt><strong>y</strong><span class="classifier">Ignored</span></dt><dd></dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt>self</dt><dd></dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="sklearn.cluster.FeatureAgglomeration.fit_predict">
<em class="property">property </em><code class="sig-name descname">fit_predict</code><a class="headerlink" href="#sklearn.cluster.FeatureAgglomeration.fit_predict" title="Permalink to this definition">¶</a></dt>
<dd><p>Fit the hierarchical clustering from features or distance matrix,
and return cluster labels.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</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</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 id="sklearn.cluster.FeatureAgglomeration.fit_transform">
<code class="sig-name descname">fit_transform</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">X</span></em>, <em class="sig-param"><span class="n">y</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">fit_params</span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/23d876161/sklearn/base.py#L671"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.FeatureAgglomeration.fit_transform" title="Permalink to this definition">¶</a></dt>
<dd><p>Fit to data, then transform it.</p>
<p>Fits transformer to <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code> with optional parameters <code class="docutils literal notranslate"><span class="pre">fit_params</span></code>
and returns a transformed version of <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like of shape (n_samples, n_features)</span></dt><dd><p>Input samples.</p>
</dd>
<dt><strong>y</strong><span class="classifier">array-like of shape (n_samples,) or (n_samples, n_outputs), default=None</span></dt><dd><p>Target values (None for unsupervised transformations).</p>
</dd>
<dt><strong>**fit_params</strong><span class="classifier">dict</span></dt><dd><p>Additional fit parameters.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>X_new</strong><span class="classifier">ndarray array of shape (n_samples, n_features_new)</span></dt><dd><p>Transformed array.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="sklearn.cluster.FeatureAgglomeration.get_params">
<code class="sig-name descname">get_params</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">deep</span><span class="o">=</span><span class="default_value">True</span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/23d876161/sklearn/base.py#L178"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.FeatureAgglomeration.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</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</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 id="sklearn.cluster.FeatureAgglomeration.inverse_transform">
<code class="sig-name descname">inverse_transform</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">Xred</span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/23d876161/sklearn/cluster/_feature_agglomeration.py#L53"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.FeatureAgglomeration.inverse_transform" title="Permalink to this definition">¶</a></dt>
<dd><p>Inverse the transformation.
Return a vector of size nb_features with the values of Xred assigned
to each group of features</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>Xred</strong><span class="classifier">array-like of shape (n_samples, n_clusters) or (n_clusters,)</span></dt><dd><p>The values to be assigned to each cluster of samples</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>X</strong><span class="classifier">ndarray of shape (n_samples, n_features) or (n_features,)</span></dt><dd><p>A vector of size n_samples with the values of Xred assigned to
each of the cluster of samples.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="sklearn.cluster.FeatureAgglomeration.set_params">
<code class="sig-name descname">set_params</code><span class="sig-paren">(</span><em class="sig-param"><span class="o">**</span><span class="n">params</span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/23d876161/sklearn/base.py#L202"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.FeatureAgglomeration.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</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</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>
<dl class="py method">
<dt id="sklearn.cluster.FeatureAgglomeration.transform">
<code class="sig-name descname">transform</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">X</span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/23d876161/sklearn/cluster/_feature_agglomeration.py#L23"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.FeatureAgglomeration.transform" title="Permalink to this definition">¶</a></dt>
<dd><p>Transform a new matrix using the built clustering</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</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,)</span></dt><dd><p>A M by N array of M observations in N dimensions or a length
M array of M one-dimensional observations.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>Y</strong><span class="classifier">ndarray of shape (n_samples, n_clusters) or (n_clusters,)</span></dt><dd><p>The pooled values for each feature cluster.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
</dd></dl>
<div class="section" id="examples-using-sklearn-cluster-featureagglomeration">
<h2>Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.cluster.FeatureAgglomeration</span></code><a class="headerlink" href="#examples-using-sklearn-cluster-featureagglomeration" title="Permalink to this headline">¶</a></h2>
<div class="sphx-glr-thumbcontainer" tooltip="These images how similar features are merged together using feature agglomeration."><div class="figure align-default" id="id1">
<img alt="Feature agglomeration" src="../../_images/sphx_glr_plot_digits_agglomeration_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/cluster/plot_digits_agglomeration.html#sphx-glr-auto-examples-cluster-plot-digits-agglomeration-py"><span class="std std-ref">Feature agglomeration</span></a></span><a class="headerlink" href="#id1" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example compares 2 dimensionality reduction strategies:"><div class="figure align-default" id="id2">
<img alt="Feature agglomeration vs. univariate selection" src="../../_images/sphx_glr_plot_feature_agglomeration_vs_univariate_selection_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/cluster/plot_feature_agglomeration_vs_univariate_selection.html#sphx-glr-auto-examples-cluster-plot-feature-agglomeration-vs-univariate-selection-py"><span class="std std-ref">Feature agglomeration vs. univariate selection</span></a></span><a class="headerlink" href="#id2" title="Permalink to this image">¶</a></p>
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