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<li><a class="reference internal" href="#"><code class="docutils literal"><span class="pre">sklearn.cluster</span></code>.MiniBatchKMeans</a><ul>
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<div class="section" id="sklearn-cluster-minibatchkmeans">
<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>.MiniBatchKMeans<a class="headerlink" href="#sklearn-cluster-minibatchkmeans" title="Permalink to this headline">¶</a></h1>
<dl class="class">
<dt id="sklearn.cluster.MiniBatchKMeans">
<em class="property">class </em><code class="descclassname">sklearn.cluster.</code><code class="descname">MiniBatchKMeans</code><span class="sig-paren">(</span><em>n_clusters=8</em>, <em>init='k-means++'</em>, <em>max_iter=100</em>, <em>batch_size=100</em>, <em>verbose=0</em>, <em>compute_labels=True</em>, <em>random_state=None</em>, <em>tol=0.0</em>, <em>max_no_improvement=10</em>, <em>init_size=None</em>, <em>n_init=3</em>, <em>reassignment_ratio=0.01</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/2d7cd0c/sklearn/cluster/k_means_.py#L1303"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.MiniBatchKMeans" title="Permalink to this definition">¶</a></dt>
<dd><p>Mini-Batch K-Means clustering</p>
<p>Read more in the <a class="reference internal" href="../clustering.html#mini-batch-kmeans"><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, optional, default: 8</p>
<blockquote>
<div><p>The number of clusters to form as well as the number of
centroids to generate.</p>
</div></blockquote>
<p><strong>init</strong> : {‘k-means++’, ‘random’ or an ndarray}, default: ‘k-means++’</p>
<blockquote>
<div><p>Method for initialization, defaults to ‘k-means++’:</p>
<p>‘k-means++’ : selects initial cluster centers for k-mean
clustering in a smart way to speed up convergence. See section
Notes in k_init for more details.</p>
<p>‘random’: choose k observations (rows) at random from data for
the initial centroids.</p>
<p>If an ndarray is passed, it should be of shape (n_clusters, n_features)
and gives the initial centers.</p>
</div></blockquote>
<p><strong>max_iter</strong> : int, optional</p>
<blockquote>
<div><p>Maximum number of iterations over the complete dataset before
stopping independently of any early stopping criterion heuristics.</p>
</div></blockquote>
<p><strong>batch_size</strong> : int, optional, default: 100</p>
<blockquote>
<div><p>Size of the mini batches.</p>
</div></blockquote>
<p><strong>verbose</strong> : boolean, optional</p>
<blockquote>
<div><p>Verbosity mode.</p>
</div></blockquote>
<p><strong>compute_labels</strong> : boolean, default=True</p>
<blockquote>
<div><p>Compute label assignment and inertia for the complete dataset
once the minibatch optimization has converged in fit.</p>
</div></blockquote>
<p><strong>random_state</strong> : int, RandomState instance or None (default)</p>
<blockquote>
<div><p>Determines random number generation for centroid initialization and
random reassignment. Use an int to make the randomness deterministic.
See <a class="reference internal" href="../../glossary.html#term-random-state"><span class="xref std std-term">Glossary</span></a>.</p>
</div></blockquote>
<p><strong>tol</strong> : float, default: 0.0</p>
<blockquote>
<div><p>Control early stopping based on the relative center changes as
measured by a smoothed, variance-normalized of the mean center
squared position changes. This early stopping heuristics is
closer to the one used for the batch variant of the algorithms
but induces a slight computational and memory overhead over the
inertia heuristic.</p>
<p>To disable convergence detection based on normalized center
change, set tol to 0.0 (default).</p>
</div></blockquote>
<p><strong>max_no_improvement</strong> : int, default: 10</p>
<blockquote>
<div><p>Control early stopping based on the consecutive number of mini
batches that does not yield an improvement on the smoothed inertia.</p>
<p>To disable convergence detection based on inertia, set
max_no_improvement to None.</p>
</div></blockquote>
<p><strong>init_size</strong> : int, optional, default: 3 * batch_size</p>
<blockquote>
<div><p>Number of samples to randomly sample for speeding up the
initialization (sometimes at the expense of accuracy): the
only algorithm is initialized by running a batch KMeans on a
random subset of the data. This needs to be larger than n_clusters.</p>
</div></blockquote>
<p><strong>n_init</strong> : int, default=3</p>
<blockquote>
<div><p>Number of random initializations that are tried.
In contrast to KMeans, the algorithm is only run once, using the
best of the <code class="docutils literal"><span class="pre">n_init</span></code> initializations as measured by inertia.</p>
</div></blockquote>
<p><strong>reassignment_ratio</strong> : float, default: 0.01</p>
<blockquote class="last">
<div><p>Control the fraction of the maximum number of counts for a
center to be reassigned. A higher value means that low count
centers are more easily reassigned, which means that the
model will take longer to converge, but should converge in a
better clustering.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
<p class="rubric">Attributes</p>
<table border="1" class="docutils">
<colgroup>
<col width="9%" />
<col width="91%" />
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><strong>cluster_centers_</strong></td>
<td>(array, [n_clusters, n_features]) Coordinates of cluster centers</td>
</tr>
<tr class="row-even"><td><strong>labels_ :</strong></td>
<td>Labels of each point (if compute_labels is set to True).</td>
</tr>
<tr class="row-odd"><td><strong>inertia_</strong></td>
<td>(float) The value of the inertia criterion associated with the chosen partition (if compute_labels is set to True). The inertia is defined as the sum of square distances of samples to their nearest neighbor.</td>
</tr>
</tbody>
</table>
<div class="admonition seealso">
<p class="first admonition-title">See also</p>
<dl class="last docutils">
<dt><a class="reference internal" href="sklearn.cluster.KMeans.html#sklearn.cluster.KMeans" title="sklearn.cluster.KMeans"><code class="xref py py-obj docutils literal"><span class="pre">KMeans</span></code></a></dt>
<dd>The classic implementation of the clustering method based on the Lloyd’s algorithm. It consumes the whole set of input data at each iteration.</dd>
</dl>
</div>
<p class="rubric">Notes</p>
<p>See <a class="reference external" href="http://www.eecs.tufts.edu/~dsculley/papers/fastkmeans.pdf">http://www.eecs.tufts.edu/~dsculley/papers/fastkmeans.pdf</a></p>
<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.MiniBatchKMeans.fit" title="sklearn.cluster.MiniBatchKMeans.fit"><code class="xref py py-obj docutils literal"><span class="pre">fit</span></code></a>(X[, y, sample_weight])</td>
<td>Compute the centroids on X by chunking it into mini-batches.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="#sklearn.cluster.MiniBatchKMeans.fit_predict" title="sklearn.cluster.MiniBatchKMeans.fit_predict"><code class="xref py py-obj docutils literal"><span class="pre">fit_predict</span></code></a>(X[, y, sample_weight])</td>
<td>Compute cluster centers and predict cluster index for each sample.</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="#sklearn.cluster.MiniBatchKMeans.fit_transform" title="sklearn.cluster.MiniBatchKMeans.fit_transform"><code class="xref py py-obj docutils literal"><span class="pre">fit_transform</span></code></a>(X[, y, sample_weight])</td>
<td>Compute clustering and transform X to cluster-distance space.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="#sklearn.cluster.MiniBatchKMeans.get_params" title="sklearn.cluster.MiniBatchKMeans.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-odd"><td><a class="reference internal" href="#sklearn.cluster.MiniBatchKMeans.partial_fit" title="sklearn.cluster.MiniBatchKMeans.partial_fit"><code class="xref py py-obj docutils literal"><span class="pre">partial_fit</span></code></a>(X[, y, sample_weight])</td>
<td>Update k means estimate on a single mini-batch X.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="#sklearn.cluster.MiniBatchKMeans.predict" title="sklearn.cluster.MiniBatchKMeans.predict"><code class="xref py py-obj docutils literal"><span class="pre">predict</span></code></a>(X[, sample_weight])</td>
<td>Predict the closest cluster each sample in X belongs to.</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="#sklearn.cluster.MiniBatchKMeans.score" title="sklearn.cluster.MiniBatchKMeans.score"><code class="xref py py-obj docutils literal"><span class="pre">score</span></code></a>(X[, y, sample_weight])</td>
<td>Opposite of the value of X on the K-means objective.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="#sklearn.cluster.MiniBatchKMeans.set_params" title="sklearn.cluster.MiniBatchKMeans.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.MiniBatchKMeans.transform" title="sklearn.cluster.MiniBatchKMeans.transform"><code class="xref py py-obj docutils literal"><span class="pre">transform</span></code></a>(X)</td>
<td>Transform X to a cluster-distance space.</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="sklearn.cluster.MiniBatchKMeans.__init__">
<code class="descname">__init__</code><span class="sig-paren">(</span><em>n_clusters=8</em>, <em>init='k-means++'</em>, <em>max_iter=100</em>, <em>batch_size=100</em>, <em>verbose=0</em>, <em>compute_labels=True</em>, <em>random_state=None</em>, <em>tol=0.0</em>, <em>max_no_improvement=10</em>, <em>init_size=None</em>, <em>n_init=3</em>, <em>reassignment_ratio=0.01</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/2d7cd0c/sklearn/cluster/k_means_.py#L1412"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.MiniBatchKMeans.__init__" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="sklearn.cluster.MiniBatchKMeans.fit">
<code class="descname">fit</code><span class="sig-paren">(</span><em>X</em>, <em>y=None</em>, <em>sample_weight=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/2d7cd0c/sklearn/cluster/k_means_.py#L1427"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.MiniBatchKMeans.fit" title="Permalink to this definition">¶</a></dt>
<dd><p>Compute the centroids on X by chunking it into mini-batches.</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 or sparse matrix, shape=(n_samples, n_features)</p>
<blockquote>
<div><p>Training instances to cluster. It must be noted that the data
will be converted to C ordering, which will cause a memory copy
if the given data is not C-contiguous.</p>
</div></blockquote>
<p><strong>y</strong> : Ignored</p>
<p><strong>sample_weight</strong> : array-like, shape (n_samples,), optional</p>
<blockquote class="last">
<div><p>The weights for each observation in X. If None, all observations
are assigned equal weight (default: None)</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="sklearn.cluster.MiniBatchKMeans.fit_predict">
<code class="descname">fit_predict</code><span class="sig-paren">(</span><em>X</em>, <em>y=None</em>, <em>sample_weight=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/2d7cd0c/sklearn/cluster/k_means_.py#L962"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.MiniBatchKMeans.fit_predict" title="Permalink to this definition">¶</a></dt>
<dd><p>Compute cluster centers and predict cluster index for each sample.</p>
<p>Convenience method; equivalent to calling fit(X) followed by
predict(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> : {array-like, sparse matrix}, shape = [n_samples, n_features]</p>
<blockquote>
<div><p>New data to transform.</p>
</div></blockquote>
<p><strong>y</strong> : Ignored</p>
<p><strong>sample_weight</strong> : array-like, shape (n_samples,), optional</p>
<blockquote>
<div><p>The weights for each observation in X. If None, all observations
are assigned equal weight (default: None)</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>labels</strong> : array, shape [n_samples,]</p>
<blockquote class="last">
<div><p>Index of the cluster each sample belongs to.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="sklearn.cluster.MiniBatchKMeans.fit_transform">
<code class="descname">fit_transform</code><span class="sig-paren">(</span><em>X</em>, <em>y=None</em>, <em>sample_weight=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/2d7cd0c/sklearn/cluster/k_means_.py#L986"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.MiniBatchKMeans.fit_transform" title="Permalink to this definition">¶</a></dt>
<dd><p>Compute clustering and transform X to cluster-distance space.</p>
<p>Equivalent to fit(X).transform(X), but more efficiently implemented.</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, sparse matrix}, shape = [n_samples, n_features]</p>
<blockquote>
<div><p>New data to transform.</p>
</div></blockquote>
<p><strong>y</strong> : Ignored</p>
<p><strong>sample_weight</strong> : array-like, shape (n_samples,), optional</p>
<blockquote>
<div><p>The weights for each observation in X. If None, all observations
are assigned equal weight (default: None)</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> : array, shape [n_samples, k]</p>
<blockquote class="last">
<div><p>X transformed in the new space.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="sklearn.cluster.MiniBatchKMeans.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.MiniBatchKMeans.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.MiniBatchKMeans.partial_fit">
<code class="descname">partial_fit</code><span class="sig-paren">(</span><em>X</em>, <em>y=None</em>, <em>sample_weight=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/2d7cd0c/sklearn/cluster/k_means_.py#L1609"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.MiniBatchKMeans.partial_fit" title="Permalink to this definition">¶</a></dt>
<dd><p>Update k means estimate on a single mini-batch 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> : array-like, shape = [n_samples, n_features]</p>
<blockquote>
<div><p>Coordinates of the data points to cluster. It must be noted that
X will be copied if it is not C-contiguous.</p>
</div></blockquote>
<p><strong>y</strong> : Ignored</p>
<p><strong>sample_weight</strong> : array-like, shape (n_samples,), optional</p>
<blockquote class="last">
<div><p>The weights for each observation in X. If None, all observations
are assigned equal weight (default: None)</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="sklearn.cluster.MiniBatchKMeans.predict">
<code class="descname">predict</code><span class="sig-paren">(</span><em>X</em>, <em>sample_weight=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/2d7cd0c/sklearn/cluster/k_means_.py#L1674"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.MiniBatchKMeans.predict" title="Permalink to this definition">¶</a></dt>
<dd><p>Predict the closest cluster each sample in X belongs to.</p>
<p>In the vector quantization literature, <cite>cluster_centers_</cite> is called
the code book and each value returned by <cite>predict</cite> is the index of
the closest code in the code book.</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, sparse matrix}, shape = [n_samples, n_features]</p>
<blockquote>
<div><p>New data to predict.</p>
</div></blockquote>
<p><strong>sample_weight</strong> : array-like, shape (n_samples,), optional</p>
<blockquote>
<div><p>The weights for each observation in X. If None, all observations
are assigned equal weight (default: None)</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>labels</strong> : array, shape [n_samples,]</p>
<blockquote class="last">
<div><p>Index of the cluster each sample belongs to.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="sklearn.cluster.MiniBatchKMeans.score">
<code class="descname">score</code><span class="sig-paren">(</span><em>X</em>, <em>y=None</em>, <em>sample_weight=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/2d7cd0c/sklearn/cluster/k_means_.py#L1067"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.MiniBatchKMeans.score" title="Permalink to this definition">¶</a></dt>
<dd><p>Opposite of the value of X on the K-means objective.</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, sparse matrix}, shape = [n_samples, n_features]</p>
<blockquote>
<div><p>New data.</p>
</div></blockquote>
<p><strong>y</strong> : Ignored</p>
<p><strong>sample_weight</strong> : array-like, shape (n_samples,), optional</p>
<blockquote>
<div><p>The weights for each observation in X. If None, all observations
are assigned equal weight (default: None)</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>score</strong> : float</p>
<blockquote class="last">
<div><p>Opposite of the value of X on the K-means objective.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="sklearn.cluster.MiniBatchKMeans.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.MiniBatchKMeans.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>
<dl class="method">
<dt id="sklearn.cluster.MiniBatchKMeans.transform">
<code class="descname">transform</code><span class="sig-paren">(</span><em>X</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/2d7cd0c/sklearn/cluster/k_means_.py#L1013"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.MiniBatchKMeans.transform" title="Permalink to this definition">¶</a></dt>
<dd><p>Transform X to a cluster-distance space.</p>
<p>In the new space, each dimension is the distance to the cluster
centers. Note that even if X is sparse, the array returned by
<cite>transform</cite> will typically be dense.</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, sparse matrix}, shape = [n_samples, n_features]</p>
<blockquote>
<div><p>New data to transform.</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> : array, shape [n_samples, k]</p>
<blockquote class="last">
<div><p>X transformed in the new space.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
</dd></dl>
</dd></dl>
<div class="section" id="examples-using-sklearn-cluster-minibatchkmeans">
<h2>Examples using <code class="docutils literal"><span class="pre">sklearn.cluster.MiniBatchKMeans</span></code><a class="headerlink" href="#examples-using-sklearn-cluster-minibatchkmeans" title="Permalink to this headline">¶</a></h2>
<div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates the Spectral Co-clustering algorithm on the twenty newsgroups dataset..."><div class="figure" id="id1">
<img alt="../../_images/sphx_glr_plot_bicluster_newsgroups_thumb.png" src="../../_images/sphx_glr_plot_bicluster_newsgroups_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/bicluster/plot_bicluster_newsgroups.html#sphx-glr-auto-examples-bicluster-plot-bicluster-newsgroups-py"><span class="std std-ref">Biclustering documents with the Spectral Co-clustering algorithm</span></a></span></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example uses a large dataset of faces to learn a set of 20 x 20 images patches that consti..."><div class="figure" id="id2">
<img alt="../../_images/sphx_glr_plot_dict_face_patches_thumb.png" src="../../_images/sphx_glr_plot_dict_face_patches_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/cluster/plot_dict_face_patches.html#sphx-glr-auto-examples-cluster-plot-dict-face-patches-py"><span class="std std-ref">Online learning of a dictionary of parts of faces</span></a></span></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example compares the timing of Birch (with and without the global clustering step) and Min..."><div class="figure" id="id3">
<img alt="../../_images/sphx_glr_plot_birch_vs_minibatchkmeans_thumb.png" src="../../_images/sphx_glr_plot_birch_vs_minibatchkmeans_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/cluster/plot_birch_vs_minibatchkmeans.html#sphx-glr-auto-examples-cluster-plot-birch-vs-minibatchkmeans-py"><span class="std std-ref">Compare BIRCH and MiniBatchKMeans</span></a></span></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Evaluate the ability of k-means initializations strategies to make the algorithm convergence ro..."><div class="figure" id="id4">
<img alt="../../_images/sphx_glr_plot_kmeans_stability_low_dim_dense_thumb.png" src="../../_images/sphx_glr_plot_kmeans_stability_low_dim_dense_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/cluster/plot_kmeans_stability_low_dim_dense.html#sphx-glr-auto-examples-cluster-plot-kmeans-stability-low-dim-dense-py"><span class="std std-ref">Empirical evaluation of the impact of k-means initialization</span></a></span></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="We want to compare the performance of the MiniBatchKMeans and KMeans: the MiniBatchKMeans is fa..."><div class="figure" id="id5">
<img alt="../../_images/sphx_glr_plot_mini_batch_kmeans_thumb.png" src="../../_images/sphx_glr_plot_mini_batch_kmeans_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/cluster/plot_mini_batch_kmeans.html#sphx-glr-auto-examples-cluster-plot-mini-batch-kmeans-py"><span class="std std-ref">Comparison of the K-Means and MiniBatchKMeans clustering algorithms</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="id6">
<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>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example applies to :ref:`olivetti_faces` different unsupervised matrix decomposition (dime..."><div class="figure" id="id7">
<img alt="../../_images/sphx_glr_plot_faces_decomposition_thumb.png" src="../../_images/sphx_glr_plot_faces_decomposition_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/decomposition/plot_faces_decomposition.html#sphx-glr-auto-examples-decomposition-plot-faces-decomposition-py"><span class="std std-ref">Faces dataset decompositions</span></a></span></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This is an example showing how the scikit-learn can be used to cluster documents by topics usin..."><div class="figure" id="id8">
<img alt="../../_images/sphx_glr_plot_document_clustering_thumb.png" src="../../_images/sphx_glr_plot_document_clustering_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/text/plot_document_clustering.html#sphx-glr-auto-examples-text-plot-document-clustering-py"><span class="std std-ref">Clustering text documents using k-means</span></a></span></p>
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