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to download the full example code or to run this example in your browser via Binder</p>
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<section class="sphx-glr-example-title" id="bisecting-k-means-and-regular-k-means-performance-comparison">
<span id="sphx-glr-auto-examples-cluster-plot-bisect-kmeans-py"></span><h1>Bisecting K-Means and Regular K-Means Performance Comparison<a class="headerlink" href="#bisecting-k-means-and-regular-k-means-performance-comparison" title="Permalink to this heading">¶</a></h1>
<p>This example shows differences between Regular K-Means algorithm and Bisecting K-Means.</p>
<p>While K-Means clusterings are different when increasing n_clusters,
Bisecting K-Means clustering builds on top of the previous ones. As a result, it
tends to create clusters that have a more regular large-scale structure. This
difference can be visually observed: for all numbers of clusters, there is a
dividing line cutting the overall data cloud in two for BisectingKMeans, which is not
present for regular K-Means.</p>
<img src="../../_images/sphx_glr_plot_bisect_kmeans_001.png" srcset="../../_images/sphx_glr_plot_bisect_kmeans_001.png" alt="Bisecting K-Means : 4 clusters, Bisecting K-Means : 8 clusters, Bisecting K-Means : 16 clusters, K-Means : 4 clusters, K-Means : 8 clusters, K-Means : 16 clusters" class = "sphx-glr-single-img"/><div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.datasets.make_blobs.html#sklearn.datasets.make_blobs" title="sklearn.datasets.make_blobs" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">make_blobs</span></a>
<span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.cluster.BisectingKMeans.html#sklearn.cluster.BisectingKMeans" title="sklearn.cluster.BisectingKMeans" class="sphx-glr-backref-module-sklearn-cluster sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">BisectingKMeans</span></a><span class="p">,</span> <a href="../../modules/generated/sklearn.cluster.KMeans.html#sklearn.cluster.KMeans" title="sklearn.cluster.KMeans" class="sphx-glr-backref-module-sklearn-cluster sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">KMeans</span></a>
<span class="nb">print</span><span class="p">(</span><span class="vm">__doc__</span><span class="p">)</span>
<span class="c1"># Generate sample data</span>
<span class="n">n_samples</span> <span class="o">=</span> <span class="mi">10000</span>
<span class="n">random_state</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">X</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.datasets.make_blobs.html#sklearn.datasets.make_blobs" title="sklearn.datasets.make_blobs" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">make_blobs</span></a><span class="p">(</span><span class="n">n_samples</span><span class="o">=</span><span class="n">n_samples</span><span class="p">,</span> <span class="n">centers</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="n">random_state</span><span class="p">)</span>
<span class="c1"># Number of cluster centers for KMeans and BisectingKMeans</span>
<span class="n">n_clusters_list</span> <span class="o">=</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">16</span><span class="p">]</span>
<span class="c1"># Algorithms to compare</span>
<span class="n">clustering_algorithms</span> <span class="o">=</span> <span class="p">{</span>
<span class="s2">"Bisecting K-Means"</span><span class="p">:</span> <a href="../../modules/generated/sklearn.cluster.BisectingKMeans.html#sklearn.cluster.BisectingKMeans" title="sklearn.cluster.BisectingKMeans" class="sphx-glr-backref-module-sklearn-cluster sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">BisectingKMeans</span></a><span class="p">,</span>
<span class="s2">"K-Means"</span><span class="p">:</span> <a href="../../modules/generated/sklearn.cluster.KMeans.html#sklearn.cluster.KMeans" title="sklearn.cluster.KMeans" class="sphx-glr-backref-module-sklearn-cluster sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">KMeans</span></a><span class="p">,</span>
<span class="p">}</span>
<span class="c1"># Make subplots for each variant</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">axs</span> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span>
<span class="nb">len</span><span class="p">(</span><span class="n">clustering_algorithms</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">n_clusters_list</span><span class="p">),</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
<span class="p">)</span>
<span class="n">axs</span> <span class="o">=</span> <span class="n">axs</span><span class="o">.</span><span class="n">T</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">algorithm_name</span><span class="p">,</span> <span class="n">Algorithm</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">clustering_algorithms</span><span class="o">.</span><span class="n">items</span><span class="p">()):</span>
<span class="k">for</span> <span class="n">j</span><span class="p">,</span> <span class="n">n_clusters</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">n_clusters_list</span><span class="p">):</span>
<span class="n">algo</span> <span class="o">=</span> <span class="n">Algorithm</span><span class="p">(</span><span class="n">n_clusters</span><span class="o">=</span><span class="n">n_clusters</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="n">random_state</span><span class="p">,</span> <span class="n">n_init</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
<span class="n">algo</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="n">centers</span> <span class="o">=</span> <span class="n">algo</span><span class="o">.</span><span class="n">cluster_centers_</span>
<span class="n">axs</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">s</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="n">algo</span><span class="o">.</span><span class="n">labels_</span><span class="p">)</span>
<span class="n">axs</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">centers</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">centers</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="s2">"r"</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
<span class="n">axs</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">algorithm_name</span><span class="si">}</span><span class="s2"> : </span><span class="si">{</span><span class="n">n_clusters</span><span class="si">}</span><span class="s2"> clusters"</span><span class="p">)</span>
<span class="c1"># Hide x labels and tick labels for top plots and y ticks for right plots.</span>
<span class="k">for</span> <span class="n">ax</span> <span class="ow">in</span> <span class="n">axs</span><span class="o">.</span><span class="n">flat</span><span class="p">:</span>
<span class="n">ax</span><span class="o">.</span><span class="n">label_outer</span><span class="p">()</span>
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