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<ul>
<li><a class="reference internal" href="#"><code class="docutils literal"><span class="pre">sklearn.cluster</span></code>.DBSCAN</a><ul>
<li><a class="reference internal" href="#examples-using-sklearn-cluster-dbscan">Examples using <code class="docutils literal"><span class="pre">sklearn.cluster.DBSCAN</span></code></a></li>
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<div class="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"><span class="pre">sklearn.cluster</span></code></a>.DBSCAN<a class="headerlink" href="#sklearn-cluster-dbscan" title="Permalink to this headline">¶</a></h1>
<dl class="class">
<dt id="sklearn.cluster.DBSCAN">
<em class="property">class </em><code class="descclassname">sklearn.cluster.</code><code class="descname">DBSCAN</code><span class="sig-paren">(</span><em>eps=0.5</em>, <em>min_samples=5</em>, <em>metric='euclidean'</em>, <em>metric_params=None</em>, <em>algorithm='auto'</em>, <em>leaf_size=30</em>, <em>p=None</em>, <em>n_jobs=1</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/2d7cd0c/sklearn/cluster/dbscan_.py#L169"><span class="viewcode-link">[source]</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>
<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>eps</strong> : float, optional</p>
<blockquote>
<div><p>The maximum distance between two samples for them to be considered
as in the same neighborhood.</p>
</div></blockquote>
<p><strong>min_samples</strong> : int, optional</p>
<blockquote>
<div><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>
</div></blockquote>
<p><strong>metric</strong> : string, or callable</p>
<blockquote>
<div><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"><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 sparse matrix, in which case only “nonzero”
elements may be considered neighbors for DBSCAN.</p>
<div class="versionadded">
<p><span class="versionmodified">New in version 0.17: </span>metric <em>precomputed</em> to accept precomputed sparse matrix.</p>
</div>
</div></blockquote>
<p><strong>metric_params</strong> : dict, optional</p>
<blockquote>
<div><p>Additional keyword arguments for the metric function.</p>
<div class="versionadded">
<p><span class="versionmodified">New in version 0.19.</span></p>
</div>
</div></blockquote>
<p><strong>algorithm</strong> : {‘auto’, ‘ball_tree’, ‘kd_tree’, ‘brute’}, optional</p>
<blockquote>
<div><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>
</div></blockquote>
<p><strong>leaf_size</strong> : int, optional (default = 30)</p>
<blockquote>
<div><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>
</div></blockquote>
<p><strong>p</strong> : float, optional</p>
<blockquote>
<div><p>The power of the Minkowski metric to be used to calculate distance
between points.</p>
</div></blockquote>
<p><strong>n_jobs</strong> : int, optional (default = 1)</p>
<blockquote class="last">
<div><p>The number of parallel jobs to run.
If <code class="docutils literal"><span class="pre">-1</span></code>, then the number of jobs is set to the number of CPU cores.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
<p class="rubric">Attributes</p>
<table border="1" class="docutils">
<colgroup>
<col width="16%" />
<col width="84%" />
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><strong>core_sample_indices_</strong></td>
<td>(array, shape = [n_core_samples]) Indices of core samples.</td>
</tr>
<tr class="row-even"><td><strong>components_</strong></td>
<td>(array, shape = [n_core_samples, n_features]) Copy of each core sample found by training.</td>
</tr>
<tr class="row-odd"><td><strong>labels_</strong></td>
<td>(array, shape = [n_samples]) Cluster labels for each point in the dataset given to fit(). Noisy samples are given the label -1.</td>
</tr>
</tbody>
</table>
<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"><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"><span class="pre">NearestNeighbors.radius_neighbors_graph</span></code></a> with
<code class="docutils literal"><span class="pre">mode='distance'</span></code>, then using <code class="docutils literal"><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"><span class="pre">sample_weight</span></code> instead.</p>
<p class="rubric">References</p>
<p>Ester, M., H. P. Kriegel, J. Sander, and X. Xu, “A Density-Based
Algorithm for Discovering Clusters in Large Spatial Databases with Noise”.
In: Proceedings of the 2nd International Conference on Knowledge Discovery
and Data Mining, Portland, OR, AAAI Press, pp. 226-231. 1996</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.DBSCAN.fit" title="sklearn.cluster.DBSCAN.fit"><code class="xref py py-obj docutils literal"><span class="pre">fit</span></code></a>(X[, y, sample_weight])</td>
<td>Perform DBSCAN clustering from features or distance matrix.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="#sklearn.cluster.DBSCAN.fit_predict" title="sklearn.cluster.DBSCAN.fit_predict"><code class="xref py py-obj docutils literal"><span class="pre">fit_predict</span></code></a>(X[, y, sample_weight])</td>
<td>Performs clustering on X and returns cluster labels.</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="#sklearn.cluster.DBSCAN.get_params" title="sklearn.cluster.DBSCAN.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-even"><td><a class="reference internal" href="#sklearn.cluster.DBSCAN.set_params" title="sklearn.cluster.DBSCAN.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>
</tbody>
</table>
<dl class="method">
<dt id="sklearn.cluster.DBSCAN.__init__">
<code class="descname">__init__</code><span class="sig-paren">(</span><em>eps=0.5</em>, <em>min_samples=5</em>, <em>metric='euclidean'</em>, <em>metric_params=None</em>, <em>algorithm='auto'</em>, <em>leaf_size=30</em>, <em>p=None</em>, <em>n_jobs=1</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/2d7cd0c/sklearn/cluster/dbscan_.py#L264"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.DBSCAN.__init__" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="sklearn.cluster.DBSCAN.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/dbscan_.py#L276"><span class="viewcode-link">[source]</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>
<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 or sparse (CSR) matrix of shape (n_samples, n_features), or array of shape (n_samples, n_samples)</p>
<blockquote>
<div><p>A feature array, or array of distances between samples if
<code class="docutils literal"><span class="pre">metric='precomputed'</span></code>.</p>
</div></blockquote>
<p><strong>sample_weight</strong> : array, shape (n_samples,), optional</p>
<blockquote>
<div><p>Weight of each sample, such that a sample with a weight of at least
<code class="docutils literal"><span class="pre">min_samples</span></code> is by itself a core sample; a sample with negative
weight may inhibit its eps-neighbor from being core.
Note that weights are absolute, and default to 1.</p>
</div></blockquote>
<p class="last"><strong>y</strong> : Ignored</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="sklearn.cluster.DBSCAN.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/dbscan_.py#L306"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.DBSCAN.fit_predict" title="Permalink to this definition">¶</a></dt>
<dd><p>Performs clustering on X and returns cluster labels.</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 or sparse (CSR) matrix of shape (n_samples, n_features), or array of shape (n_samples, n_samples)</p>
<blockquote>
<div><p>A feature array, or array of distances between samples if
<code class="docutils literal"><span class="pre">metric='precomputed'</span></code>.</p>
</div></blockquote>
<p><strong>sample_weight</strong> : array, shape (n_samples,), optional</p>
<blockquote>
<div><p>Weight of each sample, such that a sample with a weight of at least
<code class="docutils literal"><span class="pre">min_samples</span></code> is by itself a core sample; a sample with negative
weight may inhibit its eps-neighbor from being core.
Note that weights are absolute, and default to 1.</p>
</div></blockquote>
<p><strong>y</strong> : Ignored</p>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>y</strong> : ndarray, shape (n_samples,)</p>
<blockquote class="last">
<div><p>cluster labels</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="sklearn.cluster.DBSCAN.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.DBSCAN.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.DBSCAN.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.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 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>
</dd></dl>
<div class="section" id="examples-using-sklearn-cluster-dbscan">
<h2>Examples using <code class="docutils literal"><span class="pre">sklearn.cluster.DBSCAN</span></code><a class="headerlink" href="#examples-using-sklearn-cluster-dbscan" title="Permalink to this headline">¶</a></h2>
<div class="sphx-glr-thumbcontainer" tooltip="Finds core samples of high density and expands clusters from them."><div class="figure" id="id1">
<img alt="../../_images/sphx_glr_plot_dbscan_thumb.png" src="../../_images/sphx_glr_plot_dbscan_thumb.png" />
<p class="caption"><span class="caption-text"><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></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="id2">
<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>
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