-
Notifications
You must be signed in to change notification settings - Fork 81
/
Copy pathsklearn.cluster.SpectralClustering.html
478 lines (432 loc) · 24.6 KB
/
sklearn.cluster.SpectralClustering.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"
"http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
<html xmlns="http://www.w3.org/1999/xhtml">
<head>
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<title>sklearn.cluster.SpectralClustering — scikit-learn 0.17.1 documentation</title>
<!-- htmltitle is before nature.css - we use this hack to load bootstrap first -->
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<link rel="stylesheet" href="../../_static/css/bootstrap.min.css" media="screen" />
<link rel="stylesheet" href="../../_static/css/bootstrap-responsive.css"/>
<link rel="stylesheet" href="../../_static/nature.css" type="text/css" />
<link rel="stylesheet" href="../../_static/pygments.css" type="text/css" />
<link rel="stylesheet" href="../../_static/gallery.css" type="text/css" />
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../../',
VERSION: '0.17.1',
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true
};
</script>
<script type="text/javascript" src="../../_static/jquery.js"></script>
<script type="text/javascript" src="../../_static/underscore.js"></script>
<script type="text/javascript" src="../../_static/doctools.js"></script>
<script type="text/javascript" src="../../_static/js/copybutton.js"></script>
<link rel="shortcut icon" href="../../_static/favicon.ico"/>
<link rel="author" title="About these documents" href="../../about.html" />
<link rel="top" title="scikit-learn 0.17.1 documentation" href="../../index.html" />
<link rel="up" title="API Reference" href="../classes.html" />
<link rel="next" title="sklearn.cluster.estimate_bandwidth" href="sklearn.cluster.estimate_bandwidth.html" />
<link rel="prev" title="sklearn.cluster.MeanShift" href="sklearn.cluster.MeanShift.html" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<script src="../../_static/js/bootstrap.min.js" type="text/javascript"></script>
<link rel="canonical" href="https://scikit-learn.org/stable/modules/generated/sklearn.cluster.SpectralClustering.html" />
<script type="text/javascript">
$("div.buttonNext, div.buttonPrevious").hover(
function () {
$(this).css('background-color', '#FF9C34');
},
function () {
$(this).css('background-color', '#A7D6E2');
}
);
</script>
</head>
<body role="document">
<div class="header-wrapper">
<div class="header">
<p class="logo"><a href="../../index.html">
<img src="../../_static/scikit-learn-logo-small.png" alt="Logo"/>
</a>
</p><div class="navbar">
<ul>
<li><a href="../../index.html">Home</a></li>
<li><a href="../../install.html">Installation</a></li>
<li class="btn-li"><div class="btn-group">
<a href="../../documentation.html">Documentation</a>
<a class="btn dropdown-toggle" data-toggle="dropdown">
<span class="caret"></span>
</a>
<ul class="dropdown-menu">
<li class="link-title">Scikit-learn 0.17 (stable)</li>
<li><a href="../../tutorial/index.html">Tutorials</a></li>
<li><a href="../../user_guide.html">User guide</a></li>
<li><a href="../classes.html">API</a></li>
<li><a href="../../faq.html">FAQ</a></li>
<li><a href="../../developers/contributing.html">Contributing</a></li>
<li class="divider"></li>
<li><a href="http://scikit-learn.org/dev/documentation.html">Scikit-learn 0.18 (development)</a></li>
<li><a href="http://scikit-learn.org/0.16/documentation.html">Scikit-learn 0.16</a></li>
<li><a href="../../_downloads/scikit-learn-docs.pdf">PDF documentation</a></li>
</ul>
</div>
</li>
<li><a href="../../auto_examples/index.html">Examples</a></li>
</ul>
<div class="search_form">
<div id="cse" style="width: 100%;"></div>
</div>
</div> <!-- end navbar --></div>
</div>
<!-- Github "fork me" ribbon -->
<a href="https://github.com/scikit-learn/scikit-learn">
<img class="fork-me"
style="position: absolute; top: 0; right: 0; border: 0;"
src="../../_static/img/forkme.png"
alt="Fork me on GitHub" />
</a>
<div class="content-wrapper">
<div class="sphinxsidebar">
<div class="sphinxsidebarwrapper">
<div class="rel">
<!-- rellinks[1:] is an ugly hack to avoid link to module
index -->
<div class="rellink">
<a href="sklearn.cluster.MeanShift.html"
accesskey="P">Previous
<br/>
<span class="smallrellink">
sklearn.clust...
</span>
<span class="hiddenrellink">
sklearn.cluster.MeanShift
</span>
</a>
</div>
<!-- Ad a link to the 'up' page -->
<div class="spacer">
</div>
<div class="rellink">
<a href="../classes.html">
Up
<br/>
<span class="smallrellink">
API Reference
</span>
<span class="hiddenrellink">
API Reference
</span>
</a>
</div>
</div>
<p class="doc-version">This documentation is for scikit-learn <strong>version 0.17.1</strong> — <a href="http://scikit-learn.org/stable/support.html#documentation-resources">Other versions</a></p>
<p class="citing">If you use the software, please consider <a href="../../about.html#citing-scikit-learn">citing scikit-learn</a>.</p>
<ul>
<li><a class="reference internal" href="#"><code class="docutils literal"><span class="pre">sklearn.cluster</span></code>.SpectralClustering</a><ul>
<li><a class="reference internal" href="#examples-using-sklearn-cluster-spectralclustering">Examples using <code class="docutils literal"><span class="pre">sklearn.cluster.SpectralClustering</span></code></a></li>
</ul>
</li>
</ul>
</div>
</div>
<input type="checkbox" id="nav-trigger" class="nav-trigger" checked />
<label for="nav-trigger"></label>
<div class="content">
<div class="documentwrapper">
<div class="bodywrapper">
<div class="body" role="main">
<div class="section" id="sklearn-cluster-spectralclustering">
<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>.SpectralClustering<a class="headerlink" href="#sklearn-cluster-spectralclustering" title="Permalink to this headline">¶</a></h1>
<dl class="class">
<dt id="sklearn.cluster.SpectralClustering">
<em class="property">class </em><code class="descclassname">sklearn.cluster.</code><code class="descname">SpectralClustering</code><span class="sig-paren">(</span><em>n_clusters=8</em>, <em>eigen_solver=None</em>, <em>random_state=None</em>, <em>n_init=10</em>, <em>gamma=1.0</em>, <em>affinity='rbf'</em>, <em>n_neighbors=10</em>, <em>eigen_tol=0.0</em>, <em>assign_labels='kmeans'</em>, <em>degree=3</em>, <em>coef0=1</em>, <em>kernel_params=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/51a765a/sklearn/cluster/spectral.py#L269"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.SpectralClustering" title="Permalink to this definition">¶</a></dt>
<dd><p>Apply clustering to a projection to the normalized laplacian.</p>
<p>In practice Spectral Clustering is very useful when the structure of
the individual clusters is highly non-convex or more generally when
a measure of the center and spread of the cluster is not a suitable
description of the complete cluster. For instance when clusters are
nested circles on the 2D plan.</p>
<p>If affinity is the adjacency matrix of a graph, this method can be
used to find normalized graph cuts.</p>
<p>When calling <code class="docutils literal"><span class="pre">fit</span></code>, an affinity matrix is constructed using either
kernel function such the Gaussian (aka RBF) kernel of the euclidean
distanced <code class="docutils literal"><span class="pre">d(X,</span> <span class="pre">X)</span></code>:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">gamma</span> <span class="o">*</span> <span class="n">d</span><span class="p">(</span><span class="n">X</span><span class="p">,</span><span class="n">X</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span>
</pre></div>
</div>
<p>or a k-nearest neighbors connectivity matrix.</p>
<p>Alternatively, using <code class="docutils literal"><span class="pre">precomputed</span></code>, a user-provided affinity
matrix can be used.</p>
<p>Read more in the <a class="reference internal" href="../clustering.html#spectral-clustering"><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>n_clusters</strong> : integer, optional</p>
<blockquote>
<div><p>The dimension of the projection subspace.</p>
</div></blockquote>
<p><strong>affinity</strong> : string, array-like or callable, default ‘rbf’</p>
<blockquote>
<div><p>If a string, this may be one of ‘nearest_neighbors’, ‘precomputed’,
‘rbf’ or one of the kernels supported by
<cite>sklearn.metrics.pairwise_kernels</cite>.</p>
<p>Only kernels that produce similarity scores (non-negative values that
increase with similarity) should be used. This property is not checked
by the clustering algorithm.</p>
</div></blockquote>
<p><strong>gamma</strong> : float</p>
<blockquote>
<div><p>Scaling factor of RBF, polynomial, exponential chi^2 and
sigmoid affinity kernel. Ignored for
<code class="docutils literal"><span class="pre">affinity='nearest_neighbors'</span></code>.</p>
</div></blockquote>
<p><strong>degree</strong> : float, default=3</p>
<blockquote>
<div><p>Degree of the polynomial kernel. Ignored by other kernels.</p>
</div></blockquote>
<p><strong>coef0</strong> : float, default=1</p>
<blockquote>
<div><p>Zero coefficient for polynomial and sigmoid kernels.
Ignored by other kernels.</p>
</div></blockquote>
<p><strong>n_neighbors</strong> : integer</p>
<blockquote>
<div><p>Number of neighbors to use when constructing the affinity matrix using
the nearest neighbors method. Ignored for <code class="docutils literal"><span class="pre">affinity='rbf'</span></code>.</p>
</div></blockquote>
<p><strong>eigen_solver</strong> : {None, ‘arpack’, ‘lobpcg’, or ‘amg’}</p>
<blockquote>
<div><p>The eigenvalue decomposition strategy to use. AMG requires pyamg
to be installed. It can be faster on very large, sparse problems,
but may also lead to instabilities</p>
</div></blockquote>
<p><strong>random_state</strong> : int seed, RandomState instance, or None (default)</p>
<blockquote>
<div><p>A pseudo random number generator used for the initialization
of the lobpcg eigen vectors decomposition when eigen_solver == ‘amg’
and by the K-Means initialization.</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>eigen_tol</strong> : float, optional, default: 0.0</p>
<blockquote>
<div><p>Stopping criterion for eigendecomposition of the Laplacian matrix
when using arpack eigen_solver.</p>
</div></blockquote>
<p><strong>assign_labels</strong> : {‘kmeans’, ‘discretize’}, default: ‘kmeans’</p>
<blockquote>
<div><p>The strategy to use to assign labels in the embedding
space. There are two ways to assign labels after the laplacian
embedding. k-means can be applied and is a popular choice. But it can
also be sensitive to initialization. Discretization is another approach
which is less sensitive to random initialization.</p>
</div></blockquote>
<p><strong>kernel_params</strong> : dictionary of string to any, optional</p>
<blockquote>
<div><p>Parameters (keyword arguments) and values for kernel passed as
callable object. Ignored by other kernels.</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Attributes:</th><td class="field-body"><p class="first"><strong>affinity_matrix_</strong> : array-like, shape (n_samples, n_samples)</p>
<blockquote>
<div><p>Affinity matrix used for clustering. Available only if after calling
<code class="docutils literal"><span class="pre">fit</span></code>.</p>
</div></blockquote>
<p><strong>labels_ :</strong> :</p>
<blockquote class="last">
<div><p>Labels of each point</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
<p class="rubric">Notes</p>
<p>If you have an affinity matrix, such as a distance matrix,
for which 0 means identical elements, and high values means
very dissimilar elements, it can be transformed in a
similarity matrix that is well suited for the algorithm by
applying the Gaussian (RBF, heat) kernel:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span> <span class="n">X</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">/</span> <span class="p">(</span><span class="mf">2.</span> <span class="o">*</span> <span class="n">delta</span> <span class="o">**</span> <span class="mi">2</span><span class="p">))</span>
</pre></div>
</div>
<p>Another alternative is to take a symmetric version of the k
nearest neighbors connectivity matrix of the points.</p>
<p>If the pyamg package is installed, it is used: this greatly
speeds up computation.</p>
<p class="rubric">References</p>
<ul class="simple">
<li>Normalized cuts and image segmentation, 2000
Jianbo Shi, Jitendra Malik
<a class="reference external" href="http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.160.2324">http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.160.2324</a></li>
<li>A Tutorial on Spectral Clustering, 2007
Ulrike von Luxburg
<a class="reference external" href="http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.165.9323">http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.165.9323</a></li>
<li>Multiclass spectral clustering, 2003
Stella X. Yu, Jianbo Shi
<a class="reference external" href="http://www1.icsi.berkeley.edu/~stellayu/publication/doc/2003kwayICCV.pdf">http://www1.icsi.berkeley.edu/~stellayu/publication/doc/2003kwayICCV.pdf</a></li>
</ul>
<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.SpectralClustering.fit" title="sklearn.cluster.SpectralClustering.fit"><code class="xref py py-obj docutils literal"><span class="pre">fit</span></code></a>(X[, y])</td>
<td>Creates an affinity matrix for X using the selected affinity, then applies spectral clustering to this affinity matrix.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="#sklearn.cluster.SpectralClustering.fit_predict" title="sklearn.cluster.SpectralClustering.fit_predict"><code class="xref py py-obj docutils literal"><span class="pre">fit_predict</span></code></a>(X[, y])</td>
<td>Performs clustering on X and returns cluster labels.</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="#sklearn.cluster.SpectralClustering.get_params" title="sklearn.cluster.SpectralClustering.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.SpectralClustering.set_params" title="sklearn.cluster.SpectralClustering.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.SpectralClustering.__init__">
<code class="descname">__init__</code><span class="sig-paren">(</span><em>n_clusters=8</em>, <em>eigen_solver=None</em>, <em>random_state=None</em>, <em>n_init=10</em>, <em>gamma=1.0</em>, <em>affinity='rbf'</em>, <em>n_neighbors=10</em>, <em>eigen_tol=0.0</em>, <em>assign_labels='kmeans'</em>, <em>degree=3</em>, <em>coef0=1</em>, <em>kernel_params=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/51a765a/sklearn/cluster/spectral.py#L395"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.SpectralClustering.__init__" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="sklearn.cluster.SpectralClustering.fit">
<code class="descname">fit</code><span class="sig-paren">(</span><em>X</em>, <em>y=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/51a765a/sklearn/cluster/spectral.py#L412"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.SpectralClustering.fit" title="Permalink to this definition">¶</a></dt>
<dd><p>Creates an affinity matrix for X using the selected affinity,
then applies spectral clustering to this affinity 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-like or sparse matrix, shape (n_samples, n_features)</p>
<blockquote class="last">
<div><p>OR, if affinity==`precomputed`, a precomputed affinity
matrix of shape (n_samples, n_samples)</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="sklearn.cluster.SpectralClustering.fit_predict">
<code class="descname">fit_predict</code><span class="sig-paren">(</span><em>X</em>, <em>y=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/51a765a/sklearn/base.py#L356"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.SpectralClustering.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> : ndarray, shape (n_samples, n_features)</p>
<blockquote>
<div><p>Input data.</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.SpectralClustering.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/51a765a/sklearn/base.py#L199"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.SpectralClustering.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.SpectralClustering.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/51a765a/sklearn/base.py#L236"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.SpectralClustering.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
<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"><strong>self</strong> :</td>
</tr>
</tbody>
</table>
</dd></dl>
</dd></dl>
<div class="section" id="examples-using-sklearn-cluster-spectralclustering">
<h2>Examples using <code class="docutils literal"><span class="pre">sklearn.cluster.SpectralClustering</span></code><a class="headerlink" href="#examples-using-sklearn-cluster-spectralclustering" title="Permalink to this headline">¶</a></h2>
<div class="thumbnailContainer" tooltip="This example aims at showing characteristics of different clustering algorithms on datasets tha..."><div class="figure" id="id1">
<a class="reference external image-reference" href="./../../auto_examples/cluster/plot_cluster_comparison.html"><img alt="../../_images/plot_cluster_comparison1.png" src="../../_images/plot_cluster_comparison1.png" /></a>
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/cluster/plot_cluster_comparison.html#example-cluster-plot-cluster-comparison-py"><span>Comparing different clustering algorithms on toy datasets</span></a></span></p>
</div>
</div><div class="clearer"></div></div>
</div>
</div>
</div>
</div>
<div class="clearer"></div>
</div>
</div>
<div class="footer">
© 2010 - 2014, scikit-learn developers (BSD License).
<a href="../../_sources/modules/generated/sklearn.cluster.SpectralClustering.txt" rel="nofollow">Show this page source</a>
</div>
<div class="rel">
<div class="buttonPrevious">
<a href="sklearn.cluster.MeanShift.html">Previous
</a>
</div>
</div>
<script type="text/javascript">
var _gaq = _gaq || [];
_gaq.push(['_setAccount', 'UA-22606712-2']);
_gaq.push(['_trackPageview']);
(function() {
var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true;
ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js';
var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s);
})();
</script>
<script src="http://www.google.com/jsapi" type="text/javascript"></script>
<script type="text/javascript"> google.load('search', '1',
{language : 'en'}); google.setOnLoadCallback(function() {
var customSearchControl = new
google.search.CustomSearchControl('016639176250731907682:tjtqbvtvij0');
customSearchControl.setResultSetSize(google.search.Search.FILTERED_CSE_RESULTSET);
var options = new google.search.DrawOptions();
options.setAutoComplete(true);
customSearchControl.draw('cse', options); }, true);
</script>
<script src="https://scikit-learn.org/versionwarning.js"></script>
</body>
</html>