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| <section id="poweriterationclustering"> |
| <h1>PowerIterationClustering<a class="headerlink" href="#poweriterationclustering" title="Permalink to this headline">¶</a></h1> |
| <dl class="py class"> |
| <dt id="pyspark.mllib.clustering.PowerIterationClustering"> |
| <em class="property">class </em><code class="sig-prename descclassname">pyspark.mllib.clustering.</code><code class="sig-name descname">PowerIterationClustering</code><a class="reference internal" href="../../_modules/pyspark/mllib/clustering.html#PowerIterationClustering"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyspark.mllib.clustering.PowerIterationClustering" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Power Iteration Clustering (PIC), a scalable graph clustering algorithm.</p> |
| <p>Developed by Lin and Cohen <a class="reference internal" href="#rfc90c67e2119-1" id="id1">[1]</a>. From the abstract:</p> |
| <blockquote> |
| <div><p>“PIC finds a very low-dimensional embedding of a |
| dataset using truncated power iteration on a normalized pair-wise |
| similarity matrix of the data.”</p> |
| </div></blockquote> |
| <div class="versionadded"> |
| <p><span class="versionmodified added">New in version 1.5.0.</span></p> |
| </div> |
| <dl class="citation"> |
| <dt class="label" id="rfc90c67e2119-1"><span class="brackets"><a class="fn-backref" href="#id1">1</a></span></dt> |
| <dd><p>Lin, Frank & Cohen, William. (2010). Power Iteration Clustering. |
| <a class="reference external" href="http://www.cs.cmu.edu/~frank/papers/icml2010-pic-final.pdf">http://www.cs.cmu.edu/~frank/papers/icml2010-pic-final.pdf</a></p> |
| </dd> |
| </dl> |
| <p class="rubric">Methods</p> |
| <table class="longtable table autosummary"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.mllib.clustering.PowerIterationClustering.train" title="pyspark.mllib.clustering.PowerIterationClustering.train"><code class="xref py py-obj docutils literal notranslate"><span class="pre">train</span></code></a>(rdd, k[, maxIterations, initMode])</p></td> |
| <td><p>Train PowerIterationClusteringModel</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Methods Documentation</p> |
| <dl class="py method"> |
| <dt id="pyspark.mllib.clustering.PowerIterationClustering.train"> |
| <em class="property">classmethod </em><code class="sig-name descname">train</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">rdd</span><span class="p">:</span> <span class="n">pyspark.rdd.RDD<span class="p">[</span>Tuple<span class="p">[</span>int<span class="p">, </span>int<span class="p">, </span>float<span class="p">]</span><span class="p">]</span></span></em>, <em class="sig-param"><span class="n">k</span><span class="p">:</span> <span class="n">int</span></em>, <em class="sig-param"><span class="n">maxIterations</span><span class="p">:</span> <span class="n">int</span> <span class="o">=</span> <span class="default_value">100</span></em>, <em class="sig-param"><span class="n">initMode</span><span class="p">:</span> <span class="n">str</span> <span class="o">=</span> <span class="default_value">'random'</span></em><span class="sig-paren">)</span> → <a class="reference internal" href="pyspark.mllib.clustering.PowerIterationClusteringModel.html#pyspark.mllib.clustering.PowerIterationClusteringModel" title="pyspark.mllib.clustering.PowerIterationClusteringModel">pyspark.mllib.clustering.PowerIterationClusteringModel</a><a class="reference internal" href="../../_modules/pyspark/mllib/clustering.html#PowerIterationClustering.train"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyspark.mllib.clustering.PowerIterationClustering.train" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Train PowerIterationClusteringModel</p> |
| <div class="versionadded"> |
| <p><span class="versionmodified added">New in version 1.5.0.</span></p> |
| </div> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>rdd</strong><span class="classifier"><a class="reference internal" href="pyspark.RDD.html#pyspark.RDD" title="pyspark.RDD"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyspark.RDD</span></code></a></span></dt><dd><p>An RDD of (i, j, s<sub>ij</sub>) tuples representing the |
| affinity matrix, which is the matrix A in the PIC paper. The |
| similarity s<sub>ij</sub>must be nonnegative. This is a symmetric |
| matrix and hence s<sub>ij</sub>= s<sub>ji</sub> For any (i, j) with |
| nonzero similarity, there should be either (i, j, s<sub>ij</sub>) or |
| (j, i, s<sub>ji</sub>) in the input. Tuples with i = j are ignored, |
| because it is assumed s<sub>ij</sub>= 0.0.</p> |
| </dd> |
| <dt><strong>k</strong><span class="classifier">int</span></dt><dd><p>Number of clusters.</p> |
| </dd> |
| <dt><strong>maxIterations</strong><span class="classifier">int, optional</span></dt><dd><p>Maximum number of iterations of the PIC algorithm. |
| (default: 100)</p> |
| </dd> |
| <dt><strong>initMode</strong><span class="classifier">str, optional</span></dt><dd><p>Initialization mode. This can be either “random” to use |
| a random vector as vertex properties, or “degree” to use |
| normalized sum similarities. |
| (default: “random”)</p> |
| </dd> |
| </dl> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| </dd></dl> |
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