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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>.cluster_optics_xi</a><ul>
<li><a class="reference internal" href="#sklearn.cluster.cluster_optics_xi"><code class="docutils literal notranslate"><span class="pre">cluster_optics_xi</span></code></a></li>
</ul>
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<section id="sklearn-cluster-cluster-optics-xi">
<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>.cluster_optics_xi<a class="headerlink" href="#sklearn-cluster-cluster-optics-xi" title="Permalink to this heading">¶</a></h1>
<dl class="py function">
<dt class="sig sig-object py" id="sklearn.cluster.cluster_optics_xi">
<span class="sig-prename descclassname"><span class="pre">sklearn.cluster.</span></span><span class="sig-name descname"><span class="pre">cluster_optics_xi</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reachability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">predecessor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ordering</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_samples</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_cluster_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xi</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">predecessor_correction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/702316c27/sklearn/cluster/_optics.py#L731"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.cluster_optics_xi" title="Permalink to this definition">¶</a></dt>
<dd><p>Automatically extract clusters according to the Xi-steep method.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>reachability</strong><span class="classifier">ndarray of shape (n_samples,)</span></dt><dd><p>Reachability distances calculated by OPTICS (<code class="docutils literal notranslate"><span class="pre">reachability_</span></code>).</p>
</dd>
<dt><strong>predecessor</strong><span class="classifier">ndarray of shape (n_samples,)</span></dt><dd><p>Predecessors calculated by OPTICS.</p>
</dd>
<dt><strong>ordering</strong><span class="classifier">ndarray of shape (n_samples,)</span></dt><dd><p>OPTICS ordered point indices (<code class="docutils literal notranslate"><span class="pre">ordering_</span></code>).</p>
</dd>
<dt><strong>min_samples</strong><span class="classifier">int > 1 or float between 0 and 1</span></dt><dd><p>The same as the min_samples given to OPTICS. Up and down steep regions
can’t have more then <code class="docutils literal notranslate"><span class="pre">min_samples</span></code> consecutive non-steep points.
Expressed as an absolute number or a fraction of the number of samples
(rounded to be at least 2).</p>
</dd>
<dt><strong>min_cluster_size</strong><span class="classifier">int > 1 or float between 0 and 1, default=None</span></dt><dd><p>Minimum number of samples in an OPTICS cluster, expressed as an
absolute number or a fraction of the number of samples (rounded to be
at least 2). If <code class="docutils literal notranslate"><span class="pre">None</span></code>, the value of <code class="docutils literal notranslate"><span class="pre">min_samples</span></code> is used instead.</p>
</dd>
<dt><strong>xi</strong><span class="classifier">float between 0 and 1, default=0.05</span></dt><dd><p>Determines the minimum steepness on the reachability plot that
constitutes a cluster boundary. For example, an upwards point in the
reachability plot is defined by the ratio from one point to its
successor being at most 1-xi.</p>
</dd>
<dt><strong>predecessor_correction</strong><span class="classifier">bool, default=True</span></dt><dd><p>Correct clusters based on the calculated predecessors.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>labels</strong><span class="classifier">ndarray of shape (n_samples,)</span></dt><dd><p>The labels assigned to samples. Points which are not included
in any cluster are labeled as -1.</p>
</dd>
<dt><strong>clusters</strong><span class="classifier">ndarray of shape (n_clusters, 2)</span></dt><dd><p>The list of clusters in the form of <code class="docutils literal notranslate"><span class="pre">[start,</span> <span class="pre">end]</span></code> in each row, with
all indices inclusive. The clusters are ordered according to <code class="docutils literal notranslate"><span class="pre">(end,</span>
<span class="pre">-start)</span></code> (ascending) so that larger clusters encompassing smaller
clusters come after such nested smaller clusters. Since <code class="docutils literal notranslate"><span class="pre">labels</span></code> does
not reflect the hierarchy, usually <code class="docutils literal notranslate"><span class="pre">len(clusters)</span> <span class="pre">></span>
<span class="pre">np.unique(labels)</span></code>.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
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