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<ul>
<li><a class="reference internal" href="#"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.cluster</span></code>.DBSCAN</a><ul>
<li><a class="reference internal" href="#sklearn.cluster.DBSCAN"><code class="docutils literal notranslate"><span class="pre">DBSCAN</span></code></a><ul>
<li><a class="reference internal" href="#sklearn.cluster.DBSCAN.fit"><code class="docutils literal notranslate"><span class="pre">DBSCAN.fit</span></code></a></li>
<li><a class="reference internal" href="#sklearn.cluster.DBSCAN.fit_predict"><code class="docutils literal notranslate"><span class="pre">DBSCAN.fit_predict</span></code></a></li>
<li><a class="reference internal" href="#sklearn.cluster.DBSCAN.get_metadata_routing"><code class="docutils literal notranslate"><span class="pre">DBSCAN.get_metadata_routing</span></code></a></li>
<li><a class="reference internal" href="#sklearn.cluster.DBSCAN.get_params"><code class="docutils literal notranslate"><span class="pre">DBSCAN.get_params</span></code></a></li>
<li><a class="reference internal" href="#sklearn.cluster.DBSCAN.set_fit_request"><code class="docutils literal notranslate"><span class="pre">DBSCAN.set_fit_request</span></code></a></li>
<li><a class="reference internal" href="#sklearn.cluster.DBSCAN.set_params"><code class="docutils literal notranslate"><span class="pre">DBSCAN.set_params</span></code></a></li>
</ul>
</li>
<li><a class="reference internal" href="#examples-using-sklearn-cluster-dbscan">Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.cluster.DBSCAN</span></code></a></li>
</ul>
</li>
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<section id="sklearn-cluster-dbscan">
<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>.DBSCAN<a class="headerlink" href="#sklearn-cluster-dbscan" title="Permalink to this heading">¶</a></h1>
<dl class="py class">
<dt class="sig sig-object py" id="sklearn.cluster.DBSCAN">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.cluster.</span></span><span class="sig-name descname"><span class="pre">DBSCAN</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</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">min_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'euclidean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric_params</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">algorithm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">leaf_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">30</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</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">n_jobs</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/cluster/_dbscan.py#L168"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.DBSCAN" title="Permalink to this definition">¶</a></dt>
<dd><p>Perform DBSCAN clustering from vector array or distance matrix.</p>
<p>DBSCAN - Density-Based Spatial Clustering of Applications with Noise.
Finds core samples of high density and expands clusters from them.
Good for data which contains clusters of similar density.</p>
<p>Read more in the <a class="reference internal" href="../clustering.html#dbscan"><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>
<dt><strong>eps</strong><span class="classifier">float, default=0.5</span></dt><dd><p>The maximum distance between two samples for one to be considered
as in the neighborhood of the other. This is not a maximum bound
on the distances of points within a cluster. This is the most
important DBSCAN parameter to choose appropriately for your data set
and distance function.</p>
</dd>
<dt><strong>min_samples</strong><span class="classifier">int, default=5</span></dt><dd><p>The number of samples (or total weight) in a neighborhood for a point
to be considered as a core point. This includes the point itself.</p>
</dd>
<dt><strong>metric</strong><span class="classifier">str, or callable, default=’euclidean’</span></dt><dd><p>The metric to use when calculating distance between instances in a
feature array. If metric is a string or callable, it must be one of
the options allowed by <a class="reference internal" href="sklearn.metrics.pairwise_distances.html#sklearn.metrics.pairwise_distances" title="sklearn.metrics.pairwise_distances"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.metrics.pairwise_distances</span></code></a> for
its metric parameter.
If metric is “precomputed”, X is assumed to be a distance matrix and
must be square. X may be a <a class="reference internal" href="../../glossary.html#term-sparse-graph"><span class="xref std std-term">sparse graph</span></a>, in which
case only “nonzero” elements may be considered neighbors for DBSCAN.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 0.17: </span>metric <em>precomputed</em> to accept precomputed sparse matrix.</p>
</div>
</dd>
<dt><strong>metric_params</strong><span class="classifier">dict, default=None</span></dt><dd><p>Additional keyword arguments for the metric function.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 0.19.</span></p>
</div>
</dd>
<dt><strong>algorithm</strong><span class="classifier">{‘auto’, ‘ball_tree’, ‘kd_tree’, ‘brute’}, default=’auto’</span></dt><dd><p>The algorithm to be used by the NearestNeighbors module
to compute pointwise distances and find nearest neighbors.
See NearestNeighbors module documentation for details.</p>
</dd>
<dt><strong>leaf_size</strong><span class="classifier">int, default=30</span></dt><dd><p>Leaf size passed to BallTree or cKDTree. This can affect the speed
of the construction and query, as well as the memory required
to store the tree. The optimal value depends
on the nature of the problem.</p>
</dd>
<dt><strong>p</strong><span class="classifier">float, default=None</span></dt><dd><p>The power of the Minkowski metric to be used to calculate distance
between points. If None, then <code class="docutils literal notranslate"><span class="pre">p=2</span></code> (equivalent to the Euclidean
distance).</p>
</dd>
<dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>The number of parallel jobs to run.
<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.3.1)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
<code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="../../glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
for more details.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Attributes<span class="colon">:</span></dt>
<dd class="field-even"><dl>
<dt><strong>core_sample_indices_</strong><span class="classifier">ndarray of shape (n_core_samples,)</span></dt><dd><p>Indices of core samples.</p>
</dd>
<dt><strong>components_</strong><span class="classifier">ndarray of shape (n_core_samples, n_features)</span></dt><dd><p>Copy of each core sample found by training.</p>
</dd>
<dt><strong>labels_</strong><span class="classifier">ndarray of shape (n_samples)</span></dt><dd><p>Cluster labels for each point in the dataset given to fit().
Noisy samples are given the label -1.</p>
</dd>
<dt><strong>n_features_in_</strong><span class="classifier">int</span></dt><dd><p>Number of features seen during <a class="reference internal" href="../../glossary.html#term-fit"><span class="xref std std-term">fit</span></a>.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 0.24.</span></p>
</div>
</dd>
<dt><strong>feature_names_in_</strong><span class="classifier">ndarray of shape (<code class="docutils literal notranslate"><span class="pre">n_features_in_</span></code>,)</span></dt><dd><p>Names of features seen during <a class="reference internal" href="../../glossary.html#term-fit"><span class="xref std std-term">fit</span></a>. Defined only when <code class="docutils literal notranslate"><span class="pre">X</span></code>
has feature names that are all strings.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 1.0.</span></p>
</div>
</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.cluster.OPTICS.html#sklearn.cluster.OPTICS" title="sklearn.cluster.OPTICS"><code class="xref py py-obj docutils literal notranslate"><span class="pre">OPTICS</span></code></a></dt><dd><p>A similar clustering at multiple values of eps. Our implementation is optimized for memory usage.</p>
</dd>
</dl>
</div>
<p class="rubric">Notes</p>
<p>For an example, see <a class="reference internal" href="../../auto_examples/cluster/plot_dbscan.html#sphx-glr-auto-examples-cluster-plot-dbscan-py"><span class="std std-ref">examples/cluster/plot_dbscan.py</span></a>.</p>
<p>This implementation bulk-computes all neighborhood queries, which increases
the memory complexity to O(n.d) where d is the average number of neighbors,
while original DBSCAN had memory complexity O(n). It may attract a higher
memory complexity when querying these nearest neighborhoods, depending
on the <code class="docutils literal notranslate"><span class="pre">algorithm</span></code>.</p>
<p>One way to avoid the query complexity is to pre-compute sparse
neighborhoods in chunks using
<a class="reference internal" href="sklearn.neighbors.NearestNeighbors.html#sklearn.neighbors.NearestNeighbors.radius_neighbors_graph" title="sklearn.neighbors.NearestNeighbors.radius_neighbors_graph"><code class="xref py py-func docutils literal notranslate"><span class="pre">NearestNeighbors.radius_neighbors_graph</span></code></a> with
<code class="docutils literal notranslate"><span class="pre">mode='distance'</span></code>, then using <code class="docutils literal notranslate"><span class="pre">metric='precomputed'</span></code> here.</p>
<p>Another way to reduce memory and computation time is to remove
(near-)duplicate points and use <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> instead.</p>
<p><a class="reference internal" href="sklearn.cluster.OPTICS.html#sklearn.cluster.OPTICS" title="sklearn.cluster.OPTICS"><code class="xref py py-class docutils literal notranslate"><span class="pre">OPTICS</span></code></a> provides a similar clustering with lower memory
usage.</p>
<p class="rubric">References</p>
<p>Ester, M., H. P. Kriegel, J. Sander, and X. Xu, <a class="reference external" href="https://www.dbs.ifi.lmu.de/Publikationen/Papers/KDD-96.final.frame.pdf">“A Density-Based
Algorithm for Discovering Clusters in Large Spatial Databases with Noise”</a>.
In: Proceedings of the 2nd International Conference on Knowledge Discovery
and Data Mining, Portland, OR, AAAI Press, pp. 226-231. 1996</p>
<p>Schubert, E., Sander, J., Ester, M., Kriegel, H. P., & Xu, X. (2017).
<a class="reference external" href="https://doi.org/10.1145/3068335">“DBSCAN revisited, revisited: why and how you should (still) use DBSCAN.”</a>
ACM Transactions on Database Systems (TODS), 42(3), 19.</p>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="kn">import</span> <span class="n">DBSCAN</span>
<span class="gp">>>> </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="gp">>>> </span><span class="n">X</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">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span>
<span class="gp">... </span> <span class="p">[</span><span class="mi">8</span><span class="p">,</span> <span class="mi">7</span><span class="p">],</span> <span class="p">[</span><span class="mi">8</span><span class="p">,</span> <span class="mi">8</span><span class="p">],</span> <span class="p">[</span><span class="mi">25</span><span class="p">,</span> <span class="mi">80</span><span class="p">]])</span>
<span class="gp">>>> </span><span class="n">clustering</span> <span class="o">=</span> <span class="n">DBSCAN</span><span class="p">(</span><span class="n">eps</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">min_samples</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">clustering</span><span class="o">.</span><span class="n">labels_</span>
<span class="go">array([ 0, 0, 0, 1, 1, -1])</span>
<span class="gp">>>> </span><span class="n">clustering</span>
<span class="go">DBSCAN(eps=3, min_samples=2)</span>
</pre></div>
</div>
<p class="rubric">Methods</p>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.cluster.DBSCAN.fit" title="sklearn.cluster.DBSCAN.fit"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit</span></code></a>(X[, y, sample_weight])</p></td>
<td><p>Perform DBSCAN clustering from features, or distance matrix.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.cluster.DBSCAN.fit_predict" title="sklearn.cluster.DBSCAN.fit_predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit_predict</span></code></a>(X[, y, sample_weight])</p></td>
<td><p>Compute clusters from a data or distance matrix and predict labels.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.cluster.DBSCAN.get_metadata_routing" title="sklearn.cluster.DBSCAN.get_metadata_routing"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_metadata_routing</span></code></a>()</p></td>
<td><p>Get metadata routing of this object.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.cluster.DBSCAN.get_params" title="sklearn.cluster.DBSCAN.get_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_params</span></code></a>([deep])</p></td>
<td><p>Get parameters for this estimator.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.cluster.DBSCAN.set_fit_request" title="sklearn.cluster.DBSCAN.set_fit_request"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_fit_request</span></code></a>(*[, sample_weight])</p></td>
<td><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.cluster.DBSCAN.set_params" title="sklearn.cluster.DBSCAN.set_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_params</span></code></a>(**params)</p></td>
<td><p>Set the parameters of this estimator.</p></td>
</tr>
</tbody>
</table>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.cluster.DBSCAN.fit">
<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</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">sample_weight</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/cluster/_dbscan.py#L341"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.DBSCAN.fit" title="Permalink to this definition">¶</a></dt>
<dd><p>Perform DBSCAN clustering from features, or distance matrix.</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>X</strong><span class="classifier">{array-like, sparse matrix} of shape (n_samples, n_features), or (n_samples, n_samples)</span></dt><dd><p>Training instances to cluster, or distances between instances if
<code class="docutils literal notranslate"><span class="pre">metric='precomputed'</span></code>. If a sparse matrix is provided, it will
be converted into a sparse <code class="docutils literal notranslate"><span class="pre">csr_matrix</span></code>.</p>
</dd>
<dt><strong>y</strong><span class="classifier">Ignored</span></dt><dd><p>Not used, present here for API consistency by convention.</p>
</dd>
<dt><strong>sample_weight</strong><span class="classifier">array-like of shape (n_samples,), default=None</span></dt><dd><p>Weight of each sample, such that a sample with a weight of at least
<code class="docutils literal notranslate"><span class="pre">min_samples</span></code> is by itself a core sample; a sample with a
negative weight may inhibit its eps-neighbor from being core.
Note that weights are absolute, and default to 1.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">object</span></dt><dd><p>Returns a fitted instance of self.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.cluster.DBSCAN.fit_predict">
<span class="sig-name descname"><span class="pre">fit_predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</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">sample_weight</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/cluster/_dbscan.py#L423"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.DBSCAN.fit_predict" title="Permalink to this definition">¶</a></dt>
<dd><p>Compute clusters from a data or distance matrix and predict labels.</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>X</strong><span class="classifier">{array-like, sparse matrix} of shape (n_samples, n_features), or (n_samples, n_samples)</span></dt><dd><p>Training instances to cluster, or distances between instances if
<code class="docutils literal notranslate"><span class="pre">metric='precomputed'</span></code>. If a sparse matrix is provided, it will
be converted into a sparse <code class="docutils literal notranslate"><span class="pre">csr_matrix</span></code>.</p>
</dd>
<dt><strong>y</strong><span class="classifier">Ignored</span></dt><dd><p>Not used, present here for API consistency by convention.</p>
</dd>
<dt><strong>sample_weight</strong><span class="classifier">array-like of shape (n_samples,), default=None</span></dt><dd><p>Weight of each sample, such that a sample with a weight of at least
<code class="docutils literal notranslate"><span class="pre">min_samples</span></code> is by itself a core sample; a sample with a
negative weight may inhibit its eps-neighbor from being core.
Note that weights are absolute, and default to 1.</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>Cluster labels. Noisy samples are given the label -1.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.cluster.DBSCAN.get_metadata_routing">
<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/702316c27/sklearn/utils/_metadata_requests.py#L1287"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.DBSCAN.get_metadata_routing" title="Permalink to this definition">¶</a></dt>
<dd><p>Get metadata routing of this object.</p>
<p>Please check <a class="reference internal" href="../../metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
mechanism works.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>routing</strong><span class="classifier">MetadataRequest</span></dt><dd><p>A <a class="reference internal" href="sklearn.utils.metadata_routing.MetadataRequest.html#sklearn.utils.metadata_routing.MetadataRequest" title="sklearn.utils.metadata_routing.MetadataRequest"><code class="xref py py-class docutils literal notranslate"><span class="pre">MetadataRequest</span></code></a> encapsulating
routing information.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.cluster.DBSCAN.get_params">
<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</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/base.py#L178"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.DBSCAN.get_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Get parameters for this estimator.</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>deep</strong><span class="classifier">bool, default=True</span></dt><dd><p>If True, will return the parameters for this estimator and
contained subobjects that are estimators.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>params</strong><span class="classifier">dict</span></dt><dd><p>Parameter names mapped to their values.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.cluster.DBSCAN.set_fit_request">
<span class="sig-name descname"><span class="pre">set_fit_request</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">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.11)"><span class="pre">Union</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.cluster.DBSCAN" title="sklearn.cluster._dbscan.DBSCAN"><span class="pre">DBSCAN</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/702316c27/sklearn/utils/_metadata_requests.py#L1077"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.DBSCAN.set_fit_request" title="Permalink to this definition">¶</a></dt>
<dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
<p>Note that this method is only relevant if
<code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
Please see <a class="reference internal" href="../../metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
mechanism works.</p>
<p>The options for each parameter are:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">True</span></code>: metadata is requested, and passed to <code class="docutils literal notranslate"><span class="pre">fit</span></code> if provided. The request is ignored if metadata is not provided.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">False</span></code>: metadata is not requested and the meta-estimator will not pass it to <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">None</span></code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">str</span></code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.</p></li>
</ul>
<p>The default (<code class="docutils literal notranslate"><span class="pre">sklearn.utils.metadata_routing.UNCHANGED</span></code>) retains the
existing request. This allows you to change the request for some
parameters and not others.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 1.3.</span></p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>This method is only relevant if this estimator is used as a
sub-estimator of a meta-estimator, e.g. used inside a
<a class="reference internal" href="sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>. Otherwise it has no effect.</p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>sample_weight</strong><span class="classifier">str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED</span></dt><dd><p>Metadata routing for <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">object</span></dt><dd><p>The updated object.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.cluster.DBSCAN.set_params">
<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/702316c27/sklearn/base.py#L202"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.DBSCAN.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 <a class="reference internal" href="sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
parameters of the form <code class="docutils literal notranslate"><span class="pre"><component>__<parameter></span></code> so that it’s
possible to update each component of a nested object.</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>**params</strong><span class="classifier">dict</span></dt><dd><p>Estimator parameters.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">estimator instance</span></dt><dd><p>Estimator instance.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
</dd></dl>
<section id="examples-using-sklearn-cluster-dbscan">
<h2>Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.cluster.DBSCAN</span></code><a class="headerlink" href="#examples-using-sklearn-cluster-dbscan" title="Permalink to this heading">¶</a></h2>
<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This example shows characteristics of different clustering algorithms on datasets that are "int..."><img alt="" src="../../_images/sphx_glr_plot_cluster_comparison_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Comparing different clustering algorithms on toy datasets</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="DBSCAN (Density-Based Spatial Clustering of Applications with Noise) finds core samples in regi..."><img alt="" src="../../_images/sphx_glr_plot_dbscan_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/cluster/plot_dbscan.html#sphx-glr-auto-examples-cluster-plot-dbscan-py"><span class="std std-ref">Demo of DBSCAN clustering algorithm</span></a></p>
<div class="sphx-glr-thumbnail-title">Demo of DBSCAN clustering algorithm</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="In this demo we will take a look at cluster.HDBSCAN from the perspective of generalizing the cl..."><img alt="" src="../../_images/sphx_glr_plot_hdbscan_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/cluster/plot_hdbscan.html#sphx-glr-auto-examples-cluster-plot-hdbscan-py"><span class="std std-ref">Demo of HDBSCAN clustering algorithm</span></a></p>
<div class="sphx-glr-thumbnail-title">Demo of HDBSCAN clustering algorithm</div>
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