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<li> <a href="/algorithms/map-reduce/clustering/canopy-clustering.html">Canopy Clustering</a></li> - <li> <a href="/algorithms/map-reduce/clustering/cluster-dumper.html">Cluster Dumper</a></li> - <li> <a href="/algorithms/map-reduce/clustering/expectation-maximization.html">Expectation Maximization</a></li> - <li> <a href="/algorithms/map-reduce/clustering/fuzzy-k-means.html">Fuzzy K-Means</a></li> - <li> <a href="/algorithms/map-reduce/clustering/hierarchical-clustering.html">Hierarchical Clustering</a></li> - <li> <a href="/algorithms/map-reduce/clustering/k-means-clustering.html">K-Means Clustering</a></li> - <li> <a href="/algorithms/map-reduce/clustering/latent-dirichlet-allocation.html">Latent Dirichlet Allocation</a></li> - <li> <a href="/algorithms/map-reduce/clustering/llr---log-likelihood-ratio.html">Log Likelihood Ratio</a></li> - <li> <a href="/algorithms/map-reduce/clustering/spectral-clustering.html">Spectral Clustering</a></li> - <li> <a href="/algorithms/map-reduce/clustering/streaming-k-means.html">Streaming K-Means</a></li> - </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>Canopy Clustering </h1> - </div> - <h3 id="about">About</h3> + <div class="col-lg-8"> + <h3 id="about">About</h3> <p><a href="http://www.kamalnigam.com/papers/canopy-kdd00.pdf";>Canopy Clustering</a> is a very simple, fast and surprisingly accurate method for grouping @@ -340,7 +197,7 @@ outputs its canopiesâ centers</li> centers</li> </ol> -<p>The points are then clustered into these final canopies when the <code>model.cluster(inputDRM)</code> is called.</p> +<p>The points are then clustered into these final canopies when the <code class="highlighter-rouge">model.cluster(inputDRM)</code> is called.</p> <p>Some ideas can be found in <a href="https://www.youtube.com/watch?v=yjPBkvYh-ss&list=PLEFAB97242917704A";>Cluster computing and MapReduce</a> lecture video series [by Google(r)]; Canopy Clustering is discussed in <a href="https://www.youtube.com/watch?v=1ZDybXl212Q";>lecture #4</a> @@ -425,41 +282,35 @@ thresholds.</p> <h3 id="example">Example</h3> -<pre><code>val drmA = drmParallelize(dense((1.0, 1.2, 1.3, 1.4), (1.1, 1.5, 2.5, 1.0), (6.0, 5.2, -5.2, 5.3), (7.0,6.0, 5.0, 5.0), (10.0, 1.0, 20.0, -10.0))) +<div class="highlighter-rouge"><pre class="highlight"><code>val drmA = drmParallelize(dense((1.0, 1.2, 1.3, 1.4), (1.1, 1.5, 2.5, 1.0), (6.0, 5.2, -5.2, 5.3), (7.0,6.0, 5.0, 5.0), (10.0, 1.0, 20.0, -10.0))) import org.apache.mahout.math.algorithms.clustering.CanopyClustering val model = new CanopyClustering().fit(drmA, 't1 -> 6.5, 't2 -> 5.5, 'distanceMeasure -> 'Chebyshev) model.cluster(drmA).collect </code></pre> - 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