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<ul>
<li><a class="reference internal" href="#"><tt class="docutils literal"><span class="pre">sklearn.cluster</span></tt>.DBSCAN</a><ul>
<li><a class="reference internal" href="#examples-using-sklearn-cluster-dbscan">Examples using <tt class="docutils literal"><span class="pre">sklearn.cluster.DBSCAN</span></tt></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"><tt class="xref py py-mod docutils literal"><span class="pre">sklearn.cluster</span></tt></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><tt class="descclassname">sklearn.cluster.</tt><tt class="descname">DBSCAN</tt><big>(</big><em>eps=0.5</em>, <em>min_samples=5</em>, <em>metric='euclidean'</em>, <em>algorithm='auto'</em>, <em>leaf_size=30</em>, <em>p=None</em>, <em>random_state=None</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/a95203b/sklearn/cluster/dbscan_.py#L143"><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>
<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 metrics.pairwise.calculate_distance for its
metric parameter.
If metric is “precomputed”, X is assumed to be a distance matrix and
must be square.</p>
</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>random_state: numpy.RandomState, optional</strong> :</p>
<blockquote>
<div><p>Deprecated and ignored as of version 0.16, will be removed in version
0.18. DBSCAN does not use random initialization.</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Attributes:</th><td class="field-body"><p class="first"><strong>core_sample_indices_</strong> : array, shape = [n_core_samples]</p>
<blockquote>
<div><p>Indices of core samples.</p>
</div></blockquote>
<p><strong>components_</strong> : array, shape = [n_core_samples, n_features]</p>
<blockquote>
<div><p>Copy of each core sample found by training.</p>
</div></blockquote>
<p><strong>labels_</strong> : array, shape = [n_samples]</p>
<blockquote class="last">
<div><p>Cluster labels for each point in the dataset given to fit().
Noisy samples are given the label -1.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
<p class="rubric">Notes</p>
<p>See examples/cluster/plot_dbscan.py for an example.</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).</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"><tt class="xref py py-obj docutils literal"><span class="pre">fit</span></tt></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"><tt class="xref py py-obj docutils literal"><span class="pre">fit_predict</span></tt></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"><tt class="xref py py-obj docutils literal"><span class="pre">get_params</span></tt></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"><tt class="xref py py-obj docutils literal"><span class="pre">set_params</span></tt></a>(**params)</td>
<td>Set the parameters of this estimator.</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="sklearn.cluster.DBSCAN.__init__">
<tt class="descname">__init__</tt><big>(</big><em>eps=0.5</em>, <em>min_samples=5</em>, <em>metric='euclidean'</em>, <em>algorithm='auto'</em>, <em>leaf_size=30</em>, <em>p=None</em>, <em>random_state=None</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/a95203b/sklearn/cluster/dbscan_.py#L206"><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">
<tt class="descname">fit</tt><big>(</big><em>X</em>, <em>y=None</em>, <em>sample_weight=None</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/a95203b/sklearn/cluster/dbscan_.py#L216"><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
<tt class="docutils literal"><span class="pre">metric='precomputed'</span></tt>.</p>
</div></blockquote>
<p><strong>sample_weight</strong> : array, shape (n_samples,), optional</p>
<blockquote class="last">
<div><p>Weight of each sample, such that a sample with a weight of at least
<tt class="docutils literal"><span class="pre">min_samples</span></tt> 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>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="sklearn.cluster.DBSCAN.fit_predict">
<tt class="descname">fit_predict</tt><big>(</big><em>X</em>, <em>y=None</em>, <em>sample_weight=None</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/a95203b/sklearn/cluster/dbscan_.py#L242"><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
<tt class="docutils literal"><span class="pre">metric='precomputed'</span></tt>.</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
<tt class="docutils literal"><span class="pre">min_samples</span></tt> 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>
</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">
<tt class="descname">get_params</tt><big>(</big><em>deep=True</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/a95203b/sklearn/base.py#L189"><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: boolean, optional</strong> :</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">
<tt class="descname">set_params</tt><big>(</big><em>**params</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/a95203b/sklearn/base.py#L226"><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 former have parameters of the form
<tt class="docutils literal"><span class="pre"><component>__<parameter></span></tt> 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"><strong>self</strong> :</td>
</tr>
</tbody>
</table>
</dd></dl>
</dd></dl>
<div class="section" id="examples-using-sklearn-cluster-dbscan">
<h2>Examples using <tt class="docutils literal"><span class="pre">sklearn.cluster.DBSCAN</span></tt><a class="headerlink" href="#examples-using-sklearn-cluster-dbscan" title="Permalink to this headline">¶</a></h2>
<div class="thumbnailContainer" tooltip="Finds core samples of high density and expands clusters from them."><div class="figure">
<a class="reference external image-reference" href="./../../auto_examples/cluster/plot_dbscan.html"><img alt="../../_images/plot_dbscan1.png" src="../../_images/plot_dbscan1.png" /></a>
<p class="caption"><a class="reference internal" href="../../auto_examples/cluster/plot_dbscan.html#example-cluster-plot-dbscan-py"><em>Demo of DBSCAN clustering algorithm</em></a></p>
</div>
</div><div class="thumbnailContainer" tooltip="This example aims at showing characteristics of different clustering algorithms on datasets tha..."><div class="figure">
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