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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. [3] The silhouette can be calculated with any distance metric, such as the Euclidean distance or the Manhattan distance.
The average silhouette of the data is another useful criterion for assessing the natural number of clusters. The silhouette of a data instance is a measure of how closely it is matched to data within its cluster and how loosely it is matched to data of the neighboring cluster, i.e., the cluster whose average distance from the datum is lowest. [8]
Cluster analysis or clustering is the task of grouping a set ... metric facility ... The silhouette coefficient contrasts the average distance to elements in the same ...
Similar to other clustering evaluation metrics such as Silhouette score, the CH index can be used to find the optimal number of clusters k in algorithms like k-means, where the value of k is not known a priori. This can be done by following these steps: Perform clustering for different values of k. Compute the CH index for each clustering result.
The Dunn index (DI) (introduced by J. C. Dunn in 1974) is a metric for evaluating clustering algorithms. [1] [2] This is part of a group of validity indices including the Davies–Bouldin index or Silhouette index, in that it is an internal evaluation scheme, where the result is based on the clustered data itself.
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Silhouette (clustering): Silhouette analysis measures the quality of clustering and provides an insight into the separation distance between the resulting clusters. [29] A higher silhouette score indicates that the object is well matched to its own cluster and poorly matched to neighboring clusters.
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