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<ul>
<li><a class="reference internal" href="#"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.datasets</span></code>.make_biclusters</a><ul>
<li><a class="reference internal" href="#sklearn.datasets.make_biclusters"><code class="docutils literal notranslate"><span class="pre">make_biclusters</span></code></a></li>
<li><a class="reference internal" href="#examples-using-sklearn-datasets-make-biclusters">Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.datasets.make_biclusters</span></code></a></li>
</ul>
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<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 notranslate"><span class="pre">sklearn.datasets</span></code></a>.make_biclusters<a class="headerlink" href="#sklearn-datasets-make-biclusters" title="Permalink to this heading">¶</a></h1>
<dl class="py function">
<dt class="sig sig-object py" id="sklearn.datasets.make_biclusters">
<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">make_biclusters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_clusters</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">noise</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">minval</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maxval</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</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/702316c27/sklearn/datasets/_samples_generator.py#L1892"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.make_biclusters" title="Permalink to this definition">¶</a></dt>
<dd><p>Generate a constant block diagonal structure array for biclustering.</p>
<p>Read more in the <a class="reference internal" href="../../datasets/sample_generators.html#sample-generators"><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 class="simple">
<dt><strong>shape</strong><span class="classifier">tuple of shape (n_rows, n_cols)</span></dt><dd><p>The shape of the result.</p>
</dd>
<dt><strong>n_clusters</strong><span class="classifier">int</span></dt><dd><p>The number of biclusters.</p>
</dd>
<dt><strong>noise</strong><span class="classifier">float, default=0.0</span></dt><dd><p>The standard deviation of the gaussian noise.</p>
</dd>
<dt><strong>minval</strong><span class="classifier">float, default=10</span></dt><dd><p>Minimum value of a bicluster.</p>
</dd>
<dt><strong>maxval</strong><span class="classifier">float, default=100</span></dt><dd><p>Maximum value of a bicluster.</p>
</dd>
<dt><strong>shuffle</strong><span class="classifier">bool, default=True</span></dt><dd><p>Shuffle the samples.</p>
</dd>
<dt><strong>random_state</strong><span class="classifier">int, RandomState instance or None, default=None</span></dt><dd><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>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl>
<dt><strong>X</strong><span class="classifier">ndarray of shape <code class="docutils literal notranslate"><span class="pre">shape</span></code></span></dt><dd><p>The generated array.</p>
</dd>
<dt><strong>rows</strong><span class="classifier">ndarray of shape (n_clusters, X.shape[0])</span></dt><dd><p>The indicators for cluster membership of each row.</p>
</dd>
<dt><strong>cols</strong><span class="classifier">ndarray of shape (n_clusters, X.shape[1])</span></dt><dd><p>The indicators for cluster membership of each column.</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.datasets.make_checkerboard.html#sklearn.datasets.make_checkerboard" title="sklearn.datasets.make_checkerboard"><code class="xref py py-obj docutils literal notranslate"><span class="pre">make_checkerboard</span></code></a></dt><dd><p>Generate an array with block checkerboard structure for biclustering.</p>
</dd>
</dl>
</div>
<p class="rubric">References</p>
<div role="list" class="citation-list">
<div class="citation" id="r62875345314c-1" role="doc-biblioentry">
<span class="label"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></span>
<p>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.</p>
</div>
</div>
</dd></dl>
<section id="examples-using-sklearn-datasets-make-biclusters">
<h2>Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.datasets.make_biclusters</span></code><a class="headerlink" href="#examples-using-sklearn-datasets-make-biclusters" title="Permalink to this heading">¶</a></h2>
<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to generate a dataset and bicluster it using the Spectral Co-Clus..."><img alt="" src="../../_images/sphx_glr_plot_spectral_coclustering_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">A demo of the Spectral Co-Clustering algorithm</div>
</div></div><div class="clearer"></div></section>
</section>
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