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(This article was first published on <strong><a href="http://www.sthda.com/english/wiki/the-ultimate-guide-to-partitioning-clustering"> Easy Guides</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
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<p>In this first volume of <a rel="nofollow" href="http://www.sthda.com/english/wiki/simplyr" target="_blank">symplyR</a>, we are excited to share our <a rel="nofollow" href="http://www.sthda.com/english/articles/27-partitioning-clustering-essentials/" target="_blank">Practical Guides to Partioning Clustering</a>.</p>
<p><img alt="Partitioning clustering methods" src="https://i2.wp.com/www.sthda.com/english/sthda-upload/images/symplyr/partitioning-clustering.png?w=456" data-recalc-dims="1"></p>
<p>The course materials contain 3 chapters organized as follow:</p>
<p><a rel="nofollow" href="http://www.sthda.com/english/articles/27-partitioning-clustering-essentials/87-k-means-clustering-essentials/" target="_blank"><i> </i> K-Means Clustering Essentials</a></p>
<p>Contents:</p>
<ul>
<li>K-means basic ideas</li>
<li>K-means algorithm</li>
<li>Computing k-means clustering in R
<ul>
<li>Data</li>
<li>Required R packages and functions: <strong>stats::kmeans</strong>()</li>
<li>Estimating the optimal number of clusters: <strong>factoextra::fviz_nbclust</strong>()</li>
<li>Computing k-means clustering</li>
<li>Accessing to the results of kmeans() function</li>
<li>Visualizing k-means clusters: <strong>factoextra::fviz_cluster</strong>()</li>
</ul>
</li>
<li>K-means clustering advantages and disadvantages</li>
<li>Alternative to k-means clustering</li>
</ul>
<p><a rel="nofollow" href="http://www.sthda.com/english/articles/27-partitioning-clustering-essentials/88-k-medoids-essentials/" target="_blank"><i> </i> K-Medoids Essentials: PAM clustering</a></p>
<p>Contents:</p>
<ul>
<li>PAM concept</li>
<li>PAM algorithm</li>
<li>Computing PAM in R
<ul>
<li>Data</li>
<li>Required R packages and functions: <strong>cluster::pam</strong>() or <strong>fpc::pamk</strong>()</li>
<li>Estimating the optimal number of clusters: <strong>factoextra::fviz_nbclust</strong>()</li>
<li>Computing PAM clustering</li>
<li>Accessing to the results of the pam() function</li>
<li>Visualizing PAM clusters: <strong>factoextra::fviz_cluster</strong>()</li>
</ul>
</li>
</ul>
<p><a rel="nofollow" href="http://www.sthda.com/english/articles/27-partitioning-clustering-essentials/89-clara-clustering-large-applications/" target="_blank"><i> </i> CLARA – Clustering Large Applications</a></p>
<p>Contents:</p>
<ul>
<li>CLARA concept</li>
<li>CLARA Algorithm</li>
<li>Computing CLARA in R
<ul>
<li>Data format and preparation</li>
<li>Required R packages and functions: <strong>cluster::clara</strong>()</li>
<li>Estimating the optimal number of clusters: <strong>factoextra::fviz_nbclust</strong>()</li>
<li>Computing CLARA</li>
<li>Visualizing CLARA clusters: <strong>factoextra::fviz_cluster</strong>()</li>
</ul>
</li>
</ul>
<p>Example of plots:</p>
<p><img alt="K means clustering plots" width="400px" src="https://i0.wp.com/www.sthda.com/english/sthda-upload/images/symplyr/kmeans-clustering-plot.png?w=456" data-recalc-dims="1"></p>
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Licence: <a rel="nofollow" href="http://creativecommons.org/licenses/by-nc-sa/3.0/us/" target="_blank"><img alt="Licence Creative Commons" src="https://i0.wp.com/i.creativecommons.org/l/by-nc-sa/3.0/fr/88x31.png?w=456" data-recalc-dims="1"></a></p>

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