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<ul>
<li><a class="reference internal" href="#"><code class="docutils literal"><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"><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"><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="class">
<dt id="sklearn.cluster.FeatureAgglomeration">
<em class="property">class </em><code class="descclassname">sklearn.cluster.</code><code class="descname">FeatureAgglomeration</code><span class="sig-paren">(</span><em>n_clusters=2</em>, <em>affinity='euclidean'</em>, <em>memory=Memory(cachedir=None)</em>, <em>connectivity=None</em>, <em>n_components=None</em>, <em>compute_full_tree='auto'</em>, <em>linkage='ward'</em>, <em>pooling_func=<function mean></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/51a765a/sklearn/cluster/hierarchical.py#L779"><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>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>connectivity</strong> : array-like or callable, optional</p>
<blockquote>
<div><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>
</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> : Instance of joblib.Memory or string, 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>n_components</strong> : int (optional)</p>
<blockquote>
<div><p>Number of connected components. If None the number of connected
components is estimated from the connectivity matrix.
NOTE: This parameter is now directly determined from the connectivity
matrix and will be removed in 0.18</p>
</div></blockquote>
<p><strong>compute_full_tree</strong> : bool or ‘auto’, optional, default “auto”</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 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.</p>
</div></blockquote>
<p><strong>linkage</strong> : {“ward”, “complete”, “average”}, optional, default “ward”</p>
<blockquote>
<div><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>ward minimizes the variance of the clusters being merged.</li>
<li>average uses the average of the distances of each feature of
the two sets.</li>
<li>complete or maximum linkage uses the maximum distances between
all features of the two sets.</li>
</ul>
</div></blockquote>
<p><strong>pooling_func</strong> : callable, default np.mean</p>
<blockquote>
<div><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 <cite>axis=1</cite>, and reduce it to an array of size [M].</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Attributes:</th><td class="field-body"><p class="first"><strong>labels_</strong> : array-like, (n_features,)</p>
<blockquote>
<div><p>cluster labels for each feature.</p>
</div></blockquote>
<p><strong>n_leaves_</strong> : int</p>
<blockquote>
<div><p>Number of leaves in the hierarchical tree.</p>
</div></blockquote>
<p><strong>n_components_</strong> : int</p>
<blockquote>
<div><p>The estimated number of connected components in the graph.</p>
</div></blockquote>
<p><strong>children_</strong> : array-like, shape (n_nodes-1, 2)</p>
<blockquote class="last">
<div><p>The children of each non-leaf node. Values less than <cite>n_features</cite>
correspond to leaves of the tree which are the original samples.
A node <cite>i</cite> greater than or equal to <cite>n_features</cite> is a non-leaf
node and has children <cite>children_[i - n_features]</cite>. Alternatively
at the i-th iteration, children[i][0] and children[i][1]
are merged to form node <cite>n_features + i</cite></p>
</div></blockquote>
</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.FeatureAgglomeration.fit" title="sklearn.cluster.FeatureAgglomeration.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.FeatureAgglomeration.fit_transform" title="sklearn.cluster.FeatureAgglomeration.fit_transform"><code class="xref py py-obj docutils literal"><span class="pre">fit_transform</span></code></a>(X[, y])</td>
<td>Fit to data, then transform it.</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="#sklearn.cluster.FeatureAgglomeration.get_params" title="sklearn.cluster.FeatureAgglomeration.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.FeatureAgglomeration.inverse_transform" title="sklearn.cluster.FeatureAgglomeration.inverse_transform"><code class="xref py py-obj docutils literal"><span class="pre">inverse_transform</span></code></a>(Xred)</td>
<td>Inverse the transformation.</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="#sklearn.cluster.FeatureAgglomeration.pooling_func" title="sklearn.cluster.FeatureAgglomeration.pooling_func"><code class="xref py py-obj docutils literal"><span class="pre">pooling_func</span></code></a>(a[, axis, dtype, out, keepdims])</td>
<td>Compute the arithmetic mean along the specified axis.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="#sklearn.cluster.FeatureAgglomeration.set_params" title="sklearn.cluster.FeatureAgglomeration.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>
<tr class="row-odd"><td><a class="reference internal" href="#sklearn.cluster.FeatureAgglomeration.transform" title="sklearn.cluster.FeatureAgglomeration.transform"><code class="xref py py-obj docutils literal"><span class="pre">transform</span></code></a>(X[, pooling_func])</td>
<td>Transform a new matrix using the built clustering</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="sklearn.cluster.FeatureAgglomeration.__init__">
<code class="descname">__init__</code><span class="sig-paren">(</span><em>n_clusters=2</em>, <em>affinity='euclidean'</em>, <em>memory=Memory(cachedir=None)</em>, <em>connectivity=None</em>, <em>n_components=None</em>, <em>compute_full_tree='auto'</em>, <em>linkage='ward'</em>, <em>pooling_func=<function mean></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/51a765a/sklearn/cluster/hierarchical.py#L690"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.FeatureAgglomeration.__init__" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="sklearn.cluster.FeatureAgglomeration.fit">
<code class="descname">fit</code><span class="sig-paren">(</span><em>X</em>, <em>y=None</em>, <em>**params</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/51a765a/sklearn/cluster/hierarchical.py#L860"><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>
<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>The data</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last"><strong>self</strong> :</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="sklearn.cluster.FeatureAgglomeration.fit_transform">
<code class="descname">fit_transform</code><span class="sig-paren">(</span><em>X</em>, <em>y=None</em>, <em>**fit_params</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/51a765a/sklearn/base.py#L431"><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 X and y with optional parameters fit_params
and returns a transformed version of X.</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> : numpy array of shape [n_samples, n_features]</p>
<blockquote>
<div><p>Training set.</p>
</div></blockquote>
<p><strong>y</strong> : numpy array of shape [n_samples]</p>
<blockquote>
<div><p>Target values.</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>X_new</strong> : numpy array of shape [n_samples, n_features_new]</p>
<blockquote class="last">
<div><p>Transformed array.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="sklearn.cluster.FeatureAgglomeration.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/51a765a/sklearn/base.py#L199"><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>
<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: boolean, optional</strong> :</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.FeatureAgglomeration.inverse_transform">
<code class="descname">inverse_transform</code><span class="sig-paren">(</span><em>Xred</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/51a765a/sklearn/cluster/_feature_agglomeration.py#L66"><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>
<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>Xred</strong> : array-like, shape=[n_samples, n_clusters] or [n_clusters,]</p>
<blockquote>
<div><p>The values to be assigned to each cluster of samples</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>X</strong> : array, shape=[n_samples, n_features] or [n_features]</p>
<blockquote class="last">
<div><p>A vector of size n_samples with the values of Xred assigned to
each of the cluster of samples.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="sklearn.cluster.FeatureAgglomeration.pooling_func">
<code class="descname">pooling_func</code><span class="sig-paren">(</span><em>a</em>, <em>axis=None</em>, <em>dtype=None</em>, <em>out=None</em>, <em>keepdims=False</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/51a765a/sklearn/../numpy/core/fromnumeric.py#L2789"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.FeatureAgglomeration.pooling_func" title="Permalink to this definition">¶</a></dt>
<dd><p>Compute the arithmetic mean along the specified axis.</p>
<p>Returns the average of the array elements. The average is taken over
the flattened array by default, otherwise over the specified axis.
<cite>float64</cite> intermediate and return values are used for integer inputs.</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>a</strong> : array_like</p>
<blockquote>
<div><p>Array containing numbers whose mean is desired. If <cite>a</cite> is not an
array, a conversion is attempted.</p>
</div></blockquote>
<p><strong>axis</strong> : None or int or tuple of ints, optional</p>
<blockquote>
<div><p>Axis or axes along which the means are computed. The default is to
compute the mean of the flattened array.</p>
<p>If this is a tuple of ints, a mean is performed over multiple axes,
instead of a single axis or all the axes as before.</p>
</div></blockquote>
<p><strong>dtype</strong> : data-type, optional</p>
<blockquote>
<div><p>Type to use in computing the mean. For integer inputs, the default
is <cite>float64</cite>; for floating point inputs, it is the same as the
input dtype.</p>
</div></blockquote>
<p><strong>out</strong> : ndarray, optional</p>
<blockquote>
<div><p>Alternate output array in which to place the result. The default
is <code class="docutils literal"><span class="pre">None</span></code>; if provided, it must have the same shape as the
expected output, but the type will be cast if necessary.
See <cite>doc.ufuncs</cite> for details.</p>
</div></blockquote>
<p><strong>keepdims</strong> : bool, optional</p>
<blockquote>
<div><p>If this is set to True, the axes which are reduced are left
in the result as dimensions with size one. With this option,
the result will broadcast correctly against the original <cite>arr</cite>.</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>m</strong> : ndarray, see dtype parameter above</p>
<blockquote class="last">
<div><p>If <cite>out=None</cite>, returns a new array containing the mean values,
otherwise a reference to the output array is returned.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
<div class="admonition seealso">
<p class="first admonition-title">See also</p>
<dl class="docutils">
<dt><code class="xref py py-obj docutils literal"><span class="pre">average</span></code></dt>
<dd>Weighted average</dd>
</dl>
<p class="last"><code class="xref py py-obj docutils literal"><span class="pre">std</span></code>, <code class="xref py py-obj docutils literal"><span class="pre">var</span></code>, <code class="xref py py-obj docutils literal"><span class="pre">nanmean</span></code>, <code class="xref py py-obj docutils literal"><span class="pre">nanstd</span></code>, <code class="xref py py-obj docutils literal"><span class="pre">nanvar</span></code></p>
</div>
<p class="rubric">Notes</p>
<p>The arithmetic mean is the sum of the elements along the axis divided
by the number of elements.</p>
<p>Note that for floating-point input, the mean is computed using the
same precision the input has. Depending on the input data, this can
cause the results to be inaccurate, especially for <cite>float32</cite> (see
example below). Specifying a higher-precision accumulator using the
<cite>dtype</cite> keyword can alleviate this issue.</p>
<p class="rubric">Examples</p>
<div class="highlight-python"><div class="highlight"><pre><span class="gp">>>> </span><span class="n">a</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">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]])</span>
<span class="gp">>>> </span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
<span class="go">2.5</span>
<span class="gp">>>> </span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="go">array([ 2., 3.])</span>
<span class="gp">>>> </span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="go">array([ 1.5, 3.5])</span>
</pre></div>
</div>
<p>In single precision, <cite>mean</cite> can be inaccurate:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="gp">>>> </span><span class="n">a</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">512</span><span class="o">*</span><span class="mi">512</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">a</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="p">:]</span> <span class="o">=</span> <span class="mf">1.0</span>
<span class="gp">>>> </span><span class="n">a</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="p">:]</span> <span class="o">=</span> <span class="mf">0.1</span>
<span class="gp">>>> </span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
<span class="go">0.546875</span>
</pre></div>
</div>
<p>Computing the mean in float64 is more accurate:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="gp">>>> </span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
<span class="go">0.55000000074505806</span>
</pre></div>
</div>
</dd></dl>
<dl class="method">
<dt id="sklearn.cluster.FeatureAgglomeration.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/51a765a/sklearn/base.py#L236"><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 pipelines). The former 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"><strong>self</strong> :</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="sklearn.cluster.FeatureAgglomeration.transform">
<code class="descname">transform</code><span class="sig-paren">(</span><em>X</em>, <em>pooling_func=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/51a765a/sklearn/cluster/_feature_agglomeration.py#L27"><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>
<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] or [n_features]</p>
<blockquote>
<div><p>A M by N array of M observations in N dimensions or a length
M array of M one-dimensional observations.</p>
</div></blockquote>
<p><strong>pooling_func</strong> : callable, default=np.mean</p>
<blockquote>
<div><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 <cite>axis=1</cite>, and reduce it to an array of size [M].</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> : array, shape = [n_samples, n_clusters] or [n_clusters]</p>
<blockquote class="last">
<div><p>The pooled values for each feature cluster.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
</dd></dl>
</dd></dl>
<div class="section" id="examples-using-sklearn-cluster-featureagglomeration">
<h2>Examples using <code class="docutils literal"><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="thumbnailContainer" tooltip="These images how similar features are merged together using feature agglomeration. "><div class="figure" id="id1">
<a class="reference external image-reference" href="./../../auto_examples/cluster/plot_digits_agglomeration.html"><img alt="../../_images/plot_digits_agglomeration1.png" src="../../_images/plot_digits_agglomeration1.png" /></a>
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/cluster/plot_digits_agglomeration.html#example-cluster-plot-digits-agglomeration-py"><span>Feature agglomeration</span></a></span></p>
</div>
</div><div class="thumbnailContainer" tooltip="This example compares 2 dimensionality reduction strategies:"><div class="figure" id="id2">
<a class="reference external image-reference" href="./../../auto_examples/cluster/plot_feature_agglomeration_vs_univariate_selection.html"><img alt="../../_images/plot_feature_agglomeration_vs_univariate_selection1.png" src="../../_images/plot_feature_agglomeration_vs_univariate_selection1.png" /></a>
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/cluster/plot_feature_agglomeration_vs_univariate_selection.html#example-cluster-plot-feature-agglomeration-vs-univariate-selection-py"><span>Feature agglomeration vs. univariate selection</span></a></span></p>
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