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119 lines
4.7 KiB
HTML
119 lines
4.7 KiB
HTML
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<!DOCTYPE html>
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<html>
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<head>
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<meta charset="utf-8" />
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<meta name="author" content="Fredrik Danielsson, http://lostkeys.se">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<meta name="robots" content="noindex" />
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<title>Brandon Rozek</title>
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</head>
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<body>
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<aside class="main-nav">
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<nav>
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<ul>
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<li class="menuitem ">
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<a href="index.html%3Findex.html" data-shortcut="">
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Home
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</a>
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<li class="menuitem ">
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<a href="index.html%3Fcourses.html" data-shortcut="">
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Courses
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</a>
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Lab Aide
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</a>
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<li class="menuitem ">
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<a href="index.html%3Fpresentations.html" data-shortcut="">
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Presentations
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</a>
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</li>
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<li class="menuitem ">
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<a href="index.html%3Fresearch.html" data-shortcut="">
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Research
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</a>
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</li>
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<li class="menuitem ">
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<a href="index.html%3Ftranscript.html" data-shortcut="">
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Transcript
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</a>
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</li>
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</ul>
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</nav>
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</aside>
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<main class="main-content">
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<article class="article">
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<h1>K-Medoids</h1>
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<p>A medoid can be defined as the object of a cluster whose average dissimilarity to all the objects in the cluster is minimal.</p>
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<p>The K-medoids algorithm is related to k-means and the medoidshift algorithm. Both the k-means and k-medoids algorithms are partition and both attempt to minimize the distance between points in the cluster to it's center. In contrast to k-means, it chooses data points as centers and uses the Manhattan Norm to define the distance between data points instead of the Euclidean.</p>
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<p>This method is known to be more robust to noise and outliers compared to k-means since it minimizes the sum of pairwise dissimilarities instead of the sum of squared Euclidean distances.</p>
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<h2>Algorithms</h2>
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<p>There are several algorithms that have been created as an optimization to an exhaustive search. In this section, we'll discuss PAM and Voronoi iteration method.</p>
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<h3>Partitioning Around Medoids (PAM)</h3>
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<ol>
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<li>Select $k$ of the $n$ data points as medoids</li>
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<li>Associate each data point to the closes medoid</li>
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<li>While the cost of the configuration decreases:
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<ol>
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<li>For each medoid $m$, for each non-medoid data point $o$:
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<ol>
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<li>Swap $m$ and $o$, recompute the cost (sum of distances of points to their medoid)</li>
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<li>If the total cost of the configuration increased in the previous step, undo the swap</li>
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</ol></li>
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</ol></li>
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</ol>
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<h3>Voronoi Iteration Method</h3>
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<ol>
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<li>Select $k$ of the $n$ data points as medoids</li>
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<li>While the cost of the configuration decreases
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<ol>
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<li>In each cluster, make the point that minimizes the sum of distances within the cluster the medoid</li>
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<li>Reassign each point to the cluster defined by the closest medoid determined in the previous step.</li>
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</ol></li>
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</ol>
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<h3>Clustering Large Applications (CLARA</h3>
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<p>This is a variant of the PAM algorithm that relies on the sampling approach to handle large datasets. The cost of a particular cluster configuration is the mean cost of all the dissimilarities.</p>
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<h2>R Implementations</h2>
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<p>Both PAM and CLARA are defined in the <code>cluster</code> package in R.</p>
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<pre><code class="language-R">clara(x, k, metric = "euclidean", stand = FALSE, samples = 5,
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sampsize = min(n, 40 + 2 * k), trace = 0, medoids.x = TRUE,
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keep.data = medoids.x, rngR = FALSE)</code></pre>
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<pre><code class="language-R">pam(x, k, metric = "euclidean", stand = FALSE)</code></pre>
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</article>
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