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<ul>
<li><a class="reference internal" href="#"><code class="docutils literal"><span class="pre">sklearn.datasets</span></code>.make_biclusters</a><ul>
<li><a class="reference internal" href="#examples-using-sklearn-datasets-make-biclusters">Examples using <code class="docutils literal"><span class="pre">sklearn.datasets.make_biclusters</span></code></a></li>
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<div class="section" id="sklearn-datasets-make-biclusters">
<h1><a class="reference internal" href="../classes.html#module-sklearn.datasets" title="sklearn.datasets"><code class="xref py py-mod docutils literal"><span class="pre">sklearn.datasets</span></code></a>.make_biclusters<a class="headerlink" href="#sklearn-datasets-make-biclusters" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="sklearn.datasets.make_biclusters">
<code class="descclassname">sklearn.datasets.</code><code class="descname">make_biclusters</code><span class="sig-paren">(</span><em>shape</em>, <em>n_clusters</em>, <em>noise=0.0</em>, <em>minval=10</em>, <em>maxval=100</em>, <em>shuffle=True</em>, <em>random_state=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/2d7cd0c/sklearn/datasets/samples_generator.py#L1510"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.datasets.make_biclusters" title="Permalink to this definition">¶</a></dt>
<dd><p>Generate an array with constant block diagonal structure for
biclustering.</p>
<p>Read more in the <a class="reference internal" href="../../datasets/index.html#sample-generators"><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>shape</strong> : iterable (n_rows, n_cols)</p>
<blockquote>
<div><p>The shape of the result.</p>
</div></blockquote>
<p><strong>n_clusters</strong> : integer</p>
<blockquote>
<div><p>The number of biclusters.</p>
</div></blockquote>
<p><strong>noise</strong> : float, optional (default=0.0)</p>
<blockquote>
<div><p>The standard deviation of the gaussian noise.</p>
</div></blockquote>
<p><strong>minval</strong> : int, optional (default=10)</p>
<blockquote>
<div><p>Minimum value of a bicluster.</p>
</div></blockquote>
<p><strong>maxval</strong> : int, optional (default=100)</p>
<blockquote>
<div><p>Maximum value of a bicluster.</p>
</div></blockquote>
<p><strong>shuffle</strong> : boolean, optional (default=True)</p>
<blockquote>
<div><p>Shuffle the samples.</p>
</div></blockquote>
<p><strong>random_state</strong> : int, RandomState instance or None (default)</p>
<blockquote>
<div><p>Determines random number generation for dataset creation. Pass an int
for reproducible output across multiple function calls.
See <a class="reference internal" href="../../glossary.html#term-random-state"><span class="xref std std-term">Glossary</span></a>.</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 of shape <cite>shape</cite></p>
<blockquote>
<div><p>The generated array.</p>
</div></blockquote>
<p><strong>rows</strong> : array of shape (n_clusters, X.shape[0],)</p>
<blockquote>
<div><p>The indicators for cluster membership of each row.</p>
</div></blockquote>
<p><strong>cols</strong> : array of shape (n_clusters, X.shape[1],)</p>
<blockquote class="last">
<div><p>The indicators for cluster membership of each column.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
<div class="admonition seealso">
<p class="first admonition-title">See also</p>
<p class="last"><a class="reference internal" href="sklearn.datasets.make_checkerboard.html#sklearn.datasets.make_checkerboard" title="sklearn.datasets.make_checkerboard"><code class="xref py py-obj docutils literal"><span class="pre">make_checkerboard</span></code></a></p>
</div>
<p class="rubric">References</p>
<table class="docutils citation" frame="void" id="r4545" rules="none">
<colgroup><col class="label" /><col /></colgroup>
<tbody valign="top">
<tr><td class="label"><a class="fn-backref" href="#id1">[R4545]</a></td><td>Dhillon, I. S. (2001, August). Co-clustering documents and
words using bipartite spectral graph partitioning. In Proceedings
of the seventh ACM SIGKDD international conference on Knowledge
discovery and data mining (pp. 269-274). ACM.</td></tr>
</tbody>
</table>
</dd></dl>
<div class="section" id="examples-using-sklearn-datasets-make-biclusters">
<h2>Examples using <code class="docutils literal"><span class="pre">sklearn.datasets.make_biclusters</span></code><a class="headerlink" href="#examples-using-sklearn-datasets-make-biclusters" title="Permalink to this headline">¶</a></h2>
<div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to generate a dataset and bicluster it using the Spectral Co-Clus..."><div class="figure" id="id2">
<img alt="../../_images/sphx_glr_plot_spectral_coclustering_thumb.png" src="../../_images/sphx_glr_plot_spectral_coclustering_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/bicluster/plot_spectral_coclustering.html#sphx-glr-auto-examples-bicluster-plot-spectral-coclustering-py"><span class="std std-ref">A demo of the Spectral Co-Clustering algorithm</span></a></span></p>
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