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106 lines
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HTML
106 lines
5 KiB
HTML
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<h1>Silhouette</h1>
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<p>This technique validates the consistency within clusters of data. It provides a succinct graphical representation of how well each object lies in its cluster.</p>
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<p>The silhouette ranges from -1 to 1 where a high value indicates that the object is consistent within its own cluster and poorly matched to neighboring clustesr.</p>
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<p>A low or negative silhouette value can mean that the current clustering configuration has too many or too few clusters.</p>
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<h2>Definition</h2>
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<p>For each datum $i$, let $a(i)$ be the average distance of $i$ with all other data within the same cluster.</p>
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<p>$a(i)$ can be interpreted as how well $i$ is assigned to its cluster. (lower values mean better agreement)</p>
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<p>We can then define the average dissimilarity of point $i$ to a cluster $c$ as the average distance from $i$ to all points in $c$.</p>
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<p>Let $b(i)$ be the lowest average distance of $i$ to all other points in any other cluster in which i is not already a member.</p>
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<p>The cluster with this lowest average dissimilarity is said to be the neighboring cluster of $i$. From here we can define a silhouette:
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$$
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s(i) = \frac{b(i) - a(i)}{max{a(i), b(i)}}
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$$
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The average $s(i)$ over all data of a cluster is a measure of how tightly grouped all the data in the cluster are. A silhouette plot may be used to visualize the agreement between each of the data and its cluster.</p>
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<p><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/f/fd/Silhouette-plot-orange.png/800px-Silhouette-plot-orange.png" alt="img" /></p>
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<h3>Properties</h3>
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<p>Recall that $a(i)$ is a measure of how dissimilar $i$ is to its own cluster, a smaller value means that it's in agreement to its cluster. For $s(i)$ to be close to 1, we require $a(i) << b(i)$ .</p>
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<p>If $s(i)$ is close to negative one, then by the same logic we can see that $i$ would be more appropriate if it was clustered in its neighboring cluster.</p>
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<p>$s(i)$ near zero means that the datum is on the border of two natural clusters.</p>
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<h2>Determining the number of Clusters</h2>
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<p>This can also be used in helping to determine the number of clusters in a dataset. The ideal number of cluster is one that produces the highest silhouette value.</p>
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<p>Also a good indication that one has too many clusters is if there are clusters with the majority of observations being under the mean silhouette value.</p>
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<p><a href="https://kapilddatascience.wordpress.com/2015/11/10/using-silhouette-analysis-for-selecting-the-number-of-cluster-for-k-means-clustering/">https://kapilddatascience.wordpress.com/2015/11/10/using-silhouette-analysis-for-selecting-the-number-of-cluster-for-k-means-clustering/</a></p>
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<p><img src="https://kapilddatascience.files.wordpress.com/2015/11/plot_kmeans_silhouette_analysis_004.png?w=660&h=257" alt="plot_kmeans_silhouette_analysis_004" /></p>
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