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<li><a class="reference internal" href="#"><code class="docutils literal"><span class="pre">sklearn.cluster</span></code>.k_means</a></li>
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<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>.k_means<a class="headerlink" href="#sklearn-cluster-k-means" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="sklearn.cluster.k_means">
<code class="descclassname">sklearn.cluster.</code><code class="descname">k_means</code><span class="sig-paren">(</span><em>X</em>, <em>n_clusters</em>, <em>init='k-means++'</em>, <em>precompute_distances='auto'</em>, <em>n_init=10</em>, <em>max_iter=300</em>, <em>verbose=False</em>, <em>tol=0.0001</em>, <em>random_state=None</em>, <em>copy_x=True</em>, <em>n_jobs=1</em>, <em>return_n_iter=False</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/51a765a/sklearn/cluster/k_means_.py#L166"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.k_means" title="Permalink to this definition">¶</a></dt>
<dd><p>K-means clustering algorithm.</p>
<p>Read more in the <a class="reference internal" href="../clustering.html#k-means"><span>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>X</strong> : array-like or sparse matrix, shape (n_samples, n_features)</p>
<blockquote>
<div><p>The observations to cluster.</p>
</div></blockquote>
<p><strong>n_clusters</strong> : int</p>
<blockquote>
<div><p>The number of clusters to form as well as the number of
centroids to generate.</p>
</div></blockquote>
<p><strong>max_iter</strong> : int, optional, default 300</p>
<blockquote>
<div><p>Maximum number of iterations of the k-means algorithm to run.</p>
</div></blockquote>
<p><strong>n_init</strong> : int, optional, default: 10</p>
<blockquote>
<div><p>Number of time the k-means algorithm will be run with different
centroid seeds. The final results will be the best output of
n_init consecutive runs in terms of inertia.</p>
</div></blockquote>
<p><strong>init</strong> : {‘k-means++’, ‘random’, or ndarray, or a callable}, optional</p>
<blockquote>
<div><p>Method for initialization, default to ‘k-means++’:</p>
<p>‘k-means++’ : selects initial cluster centers for k-mean
clustering in a smart way to speed up convergence. See section
Notes in k_init for more details.</p>
<p>‘random’: generate k centroids from a Gaussian with mean and
variance estimated from the data.</p>
<p>If an ndarray is passed, it should be of shape (n_clusters, n_features)
and gives the initial centers.</p>
<p>If a callable is passed, it should take arguments X, k and
and a random state and return an initialization.</p>
</div></blockquote>
<p><strong>precompute_distances</strong> : {‘auto’, True, False}</p>
<blockquote>
<div><p>Precompute distances (faster but takes more memory).</p>
<p>‘auto’ : do not precompute distances if n_samples * n_clusters > 12
million. This corresponds to about 100MB overhead per job using
double precision.</p>
<p>True : always precompute distances</p>
<p>False : never precompute distances</p>
</div></blockquote>
<p><strong>tol</strong> : float, optional</p>
<blockquote>
<div><p>The relative increment in the results before declaring convergence.</p>
</div></blockquote>
<p><strong>verbose</strong> : boolean, optional</p>
<blockquote>
<div><p>Verbosity mode.</p>
</div></blockquote>
<p><strong>random_state</strong> : integer or numpy.RandomState, optional</p>
<blockquote>
<div><p>The generator used to initialize the centers. If an integer is
given, it fixes the seed. Defaults to the global numpy random
number generator.</p>
</div></blockquote>
<p><strong>copy_x</strong> : boolean, optional</p>
<blockquote>
<div><p>When pre-computing distances it is more numerically accurate to center
the data first. If copy_x is True, then the original data is not
modified. If False, the original data is modified, and put back before
the function returns, but small numerical differences may be introduced
by subtracting and then adding the data mean.</p>
</div></blockquote>
<p><strong>n_jobs</strong> : int</p>
<blockquote>
<div><p>The number of jobs to use for the computation. This works by computing
each of the n_init runs in parallel.</p>
<p>If -1 all CPUs are used. If 1 is given, no parallel computing code is
used at all, which is useful for debugging. For n_jobs below -1,
(n_cpus + 1 + n_jobs) are used. Thus for n_jobs = -2, all CPUs but one
are used.</p>
</div></blockquote>
<p><strong>return_n_iter</strong> : bool, optional</p>
<blockquote>
<div><p>Whether or not to return the number of iterations.</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>centroid</strong> : float ndarray with shape (k, n_features)</p>
<blockquote>
<div><p>Centroids found at the last iteration of k-means.</p>
</div></blockquote>
<p><strong>label</strong> : integer ndarray with shape (n_samples,)</p>
<blockquote>
<div><p>label[i] is the code or index of the centroid the
i’th observation is closest to.</p>
</div></blockquote>
<p><strong>inertia</strong> : float</p>
<blockquote>
<div><p>The final value of the inertia criterion (sum of squared distances to
the closest centroid for all observations in the training set).</p>
</div></blockquote>
<p><strong>best_n_iter: int</strong> :</p>
<blockquote class="last">
<div><p>Number of iterations corresponding to the best results.
Returned only if <cite>return_n_iter</cite> is set to True.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
</dd></dl>
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