Silhouette (clustering)

<p>Silhouette is a method of interpretation and validation of consistency within <a href="/facts/Cluster_analysis/HUeDnWPt">clusters of data</a>. The technique provides a succinct graphical representation of how well each object has been classified. It was proposed by Belgian statistician <a href="/facts/Peter_Rousseeuw/9S3uH49l">Peter Rousseeuw</a> in 1987.
</p><p>The silhouette value is a measure of how similar an object is to its own cluster (cohesion) compared to other clusters (separation). The silhouette ranges from −1 to +1, where a high value indicates that the object is well matched to its own cluster and poorly matched to neighboring clusters. If most objects have a high value, then the clustering configuration is appropriate. If many points have a low or negative value, then the clustering configuration may have too many or too few clusters. A clustering with an average silhouette width of over 0.7 is considered to be "strong", a value over 0.5 "reasonable" and over 0.25 "weak", but with increasing dimensionality of the data, it becomes difficult to achieve such high values because of the <a href="/facts/Curse_of_dimensionality/dk7SnY6L">curse of dimensionality</a>, as the distances become more similar.  The silhouette score is specialized for measuring cluster quality when the clusters are convex-shaped, and may not perform well if the data clusters have irregular shapes or are of varying sizes. The silhouette can be calculated with any <a href="/facts/Distance/hbXlaEFk">distance</a> metric, such as the <a href="/facts/Euclidean_distance/9qDoQKQe">Euclidean distance</a> or the <a href="/facts/Manhattan_distance/zU7gGUPv">Manhattan distance</a>.
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Silhouette (clustering) open-in-new

Silhouette (clustering)