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</ul> - </div> - </div> -</div> + <!--</ul>--> + </div><!-- /.navbar-collapse --> </div> +</nav> + + <div class="container mt-5 pb-4"> + + <div class="row"> - <div class="col-md-8"> - <div class="page-header"> - <h1>(Deprecated) Spectral Clustering </h1> - </div> - <h1 id="streamingkmeans-algorithm"><em>StreamingKMeans</em> algorithm</h1> + <div class="col-lg-8"> + <h1 id="streamingkmeans-algorithm"><em>StreamingKMeans</em> algorithm</h1> <p>The <em>StreamingKMeans</em> algorithm is a variant of Algorithm 1 from <a href="http://nips.cc/Conferences/2011/Program/event.php?ID=2989"; title="M. Shindler, A. Wong, A. Meyerson: Fast and Accurate k-means For Large Datasets">Shindler et al</a> and consists of two steps:</p> @@ -431,7 +288,7 @@ The algorithm can be instructed to take multiple independent runs (using the <em <p>##Usage of <em>StreamingKMeans</em></p> -<pre><code> bin/mahout streamingkmeans +<div class="highlighter-rouge"><pre class="highlight"><code> bin/mahout streamingkmeans -i <input> -o <output> -ow @@ -455,32 +312,33 @@ The algorithm can be instructed to take multiple independent runs (using the <em --startPhase <startPhase> --endPhase <endPhase> </code></pre> +</div> <p>###Details on Job-Specific Options:</p> <ul> - <li><code>--input (-i) <input></code>: Path to job input directory.</li> - <li><code>--output (-o) <output></code>: The directory pathname for output.</li> - <li><code>--overwrite (-ow)</code>: If present, overwrite the output directory before running job.</li> - <li><code>--numClusters (-k) <k></code>: The k in k-Means. Approximately this many clusters will be generated.</li> - <li><code>--estimatedNumMapClusters (-km) <estimatedNumMapClusters></code>: The estimated number of clusters to use for the Map phase of the job when running StreamingKMeans. This should be around k * log(n), where k is the final number of clusters and n is the total number of data points to cluster.</li> - <li><code>--estimatedDistanceCutoff (-e) <estimatedDistanceCutoff></code>: The initial estimated distance cutoff between two points for forming new clusters. If no value is given, itâs estimated from the data set</li> - <li><code>--maxNumIterations (-mi) <maxNumIterations></code>: The maximum number of iterations to run for the BallKMeans algorithm used by the reducer. If no value is given, defaults to 10.</li> - <li><code>--trimFraction (-tf) <trimFraction></code>: The âballâ aspect of ball k-means means that only the closest points to the centroid will actually be used for updating. The fraction of the points to be used is those points whose distance to the center is within trimFraction * distance to the closest other center. If no value is given, defaults to 0.9.</li> - <li><code>--randomInit</code> (<code>-ri</code>) Whether to use k-means++ initialization or random initialization of the seed centroids. Essentially, k-means++ provides better clusters, but takes longer, whereas random initialization takes less time, but produces worse clusters, and tends to fail more often and needs multiple runs to compare to k-means++. If set, uses the random initialization.</li> - <li><code>--ignoreWeights (-iw)</code>: Whether to correct the weights of the centroids after the clustering is done. The weights end up being wrong because of the trimFraction and possible train/test splits. In some cases, especially in a pipeline, having an accurate count of the weights is useful. If set, ignores the final weights.</li> - <li><code>--testProbability (-testp) <testProbability></code>: A double value between 0 and 1 that represents the percentage of points to be used for âtestingâ different clustering runs in the final BallKMeans step. If no value is given, defaults to 0.1</li> - <li><code>--numBallKMeansRuns (-nbkm) <numBallKMeansRuns></code>: Number of BallKMeans runs to use at the end to try to cluster the points. If no value is given, defaults to 4</li> - <li><code>--distanceMeasure (-dm) <distanceMeasure></code>: The classname of the DistanceMeasure. Default is SquaredEuclidean.</li> - <li><code>--searcherClass (-sc) <searcherClass></code>: The type of searcher to be used when performing nearest neighbor searches. Defaults to ProjectionSearch.</li> - <li><code>--numProjections (-np) <numProjections></code>: The number of projections considered in estimating the distances between vectors. Only used when the distance measure requested is either ProjectionSearch or FastProjectionSearch. If no value is given, defaults to 3.</li> - <li><code>--searchSize (-s) <searchSize></code>: In more efficient searches (non BruteSearch), not all distances are calculated for determining the nearest neighbors. The number of elements whose distances from the query vector is actually computer is proportional to searchSize. If no value is given, defaults to 1.</li> - <li><code>--reduceStreamingKMeans (-rskm)</code>: There might be too many intermediate clusters from the mapper to fit into memory, so the reducer can run another pass of StreamingKMeans to collapse them down to a fewer clusters.</li> - <li><code>--method (-xm)</code> method The execution method to use: sequential or mapreduce. Default is mapreduce.</li> - <li><code>-- help (-h)</code>: Print out help</li> - <li><code>--tempDir <tempDir></code>: Intermediate output directory.</li> - <li><code>--startPhase <startPhase></code> First phase to run.</li> - <li><code>--endPhase <endPhase></code> Last phase to run.</li> + <li><code class="highlighter-rouge">--input (-i) <input></code>: Path to job input directory.</li> + <li><code class="highlighter-rouge">--output (-o) <output></code>: The directory pathname for output.</li> + <li><code class="highlighter-rouge">--overwrite (-ow)</code>: If present, overwrite the output directory before running job.</li> + <li><code class="highlighter-rouge">--numClusters (-k) <k></code>: The k in k-Means. Approximately this many clusters will be generated.</li> + <li><code class="highlighter-rouge">--estimatedNumMapClusters (-km) <estimatedNumMapClusters></code>: The estimated number of clusters to use for the Map phase of the job when running StreamingKMeans. This should be around k * log(n), where k is the final number of clusters and n is the total number of data points to cluster.</li> + <li><code class="highlighter-rouge">--estimatedDistanceCutoff (-e) <estimatedDistanceCutoff></code>: The initial estimated distance cutoff between two points for forming new clusters. If no value is given, itâs estimated from the data set</li> + <li><code class="highlighter-rouge">--maxNumIterations (-mi) <maxNumIterations></code>: The maximum number of iterations to run for the BallKMeans algorithm used by the reducer. If no value is given, defaults to 10.</li> + <li><code class="highlighter-rouge">--trimFraction (-tf) <trimFraction></code>: The âballâ aspect of ball k-means means that only the closest points to the centroid will actually be used for updating. The fraction of the points to be used is those points whose distance to the center is within trimFraction * distance to the closest other center. If no value is given, defaults to 0.9.</li> + <li><code class="highlighter-rouge">--randomInit</code> (<code class="highlighter-rouge">-ri</code>) Whether to use k-means++ initialization or random initialization of the seed centroids. Essentially, k-means++ provides better clusters, but takes longer, whereas random initialization takes less time, but produces worse clusters, and tends to fail more often and needs multiple runs to compare to k-means++. If set, uses the random initialization.</li> + <li><code class="highlighter-rouge">--ignoreWeights (-iw)</code>: Whether to correct the weights of the centroids after the clustering is done. The weights end up being wrong because of the trimFraction and possible train/test splits. In some cases, especially in a pipeline, having an accurate count of the weights is useful. If set, ignores the final weights.</li> + <li><code class="highlighter-rouge">--testProbability (-testp) <testProbability></code>: A double value between 0 and 1 that represents the percentage of points to be used for âtestingâ different clustering runs in the final BallKMeans step. If no value is given, defaults to 0.1</li> + <li><code class="highlighter-rouge">--numBallKMeansRuns (-nbkm) <numBallKMeansRuns></code>: Number of BallKMeans runs to use at the end to try to cluster the points. If no value is given, defaults to 4</li> + <li><code class="highlighter-rouge">--distanceMeasure (-dm) <distanceMeasure></code>: The classname of the DistanceMeasure. Default is SquaredEuclidean.</li> + <li><code class="highlighter-rouge">--searcherClass (-sc) <searcherClass></code>: The type of searcher to be used when performing nearest neighbor searches. Defaults to ProjectionSearch.</li> + <li><code class="highlighter-rouge">--numProjections (-np) <numProjections></code>: The number of projections considered in estimating the distances between vectors. Only used when the distance measure requested is either ProjectionSearch or FastProjectionSearch. If no value is given, defaults to 3.</li> + <li><code class="highlighter-rouge">--searchSize (-s) <searchSize></code>: In more efficient searches (non BruteSearch), not all distances are calculated for determining the nearest neighbors. The number of elements whose distances from the query vector is actually computer is proportional to searchSize. If no value is given, defaults to 1.</li> + <li><code class="highlighter-rouge">--reduceStreamingKMeans (-rskm)</code>: There might be too many intermediate clusters from the mapper to fit into memory, so the reducer can run another pass of StreamingKMeans to collapse them down to a fewer clusters.</li> + <li><code class="highlighter-rouge">--method (-xm)</code> method The execution method to use: sequential or mapreduce. Default is mapreduce.</li> + <li><code class="highlighter-rouge">-- help (-h)</code>: Print out help</li> + <li><code class="highlighter-rouge">--tempDir <tempDir></code>: Intermediate output directory.</li> + <li><code class="highlighter-rouge">--startPhase <startPhase></code> First phase to run.</li> + <li><code class="highlighter-rouge">--endPhase <endPhase></code> Last phase to run.</li> </ul> <p>##References</p> @@ -492,32 +350,25 @@ The algorithm can be instructed to take multiple independent runs (using the <em </div> -</div> - </div> - - -</div> -<div id="footer"> - <div class="container"> - <p>© 2017 Apache Mahout - with help from <a href="http://jekyllbootstrap.com"; target="_blank" title="The Definitive Jekyll Blogging Framework">Jekyll Bootstrap</a> - and <a href="http://getbootstrap.com"; target="_blank">Bootstrap</a> - </p> </div> -</div> - - +</div> + <footer class="footer bg-light"> + <div class="container text-center small"> + Copyright © 2014-2017 The Apache Software Foundation, Licensed under the Apache License, Version 2.0. + </div> +</footer> + <script src="/assets/vendor/jquery/jquery-slim.min.js"></script> + <script src="/assets/vendor/popper/popper.min.js"></script> + <script src="/assets/vendor/bootstrap/js/bootstrap.min.js"></script> + <script src="/assets/header.js"></script> + <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js?config=TeX-AMS-MML_HTMLorMML"; type="text/javascript"></script> -<!-- Latest compiled and minified JavaScript, requires jQuery 1.x (2.x not supported in IE8) --> -<!-- Placed at the end of the document so the pages load faster --> -<script src="https://ajax.googleapis.com/ajax/libs/jquery/1.10.2/jquery.min.js";></script> -<script src="/assets/themes/mahout3/js/bootstrap.min.js"></script> </body> -</html> +</html>
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