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  2. Silhouette (clustering) - Wikipedia

    en.wikipedia.org/wiki/Silhouette_(clustering)

    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.

  3. Determining the number of clusters in a data set - Wikipedia

    en.wikipedia.org/wiki/Determining_the_number_of...

    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]

  4. Dunn index - Wikipedia

    en.wikipedia.org/wiki/Dunn_index

    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.

  5. Cluster analysis - Wikipedia

    en.wikipedia.org/wiki/Cluster_analysis

    Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in ... one could cluster the data set by the Silhouette coefficient ...

  6. k-medoids - Wikipedia

    en.wikipedia.org/wiki/K-medoids

    The "goodness" of the given value of k can be assessed with methods such as the silhouette method. The medoid of a cluster is defined as the object in the cluster whose sum (and, equivalently, the average) of dissimilarities to all the objects in the cluster is minimal, that is, it is a most centrally located point in the cluster.

  7. k-means clustering - Wikipedia

    en.wikipedia.org/wiki/K-means_clustering

    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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  9. Category:Clustering criteria - Wikipedia

    en.wikipedia.org/wiki/Category:Clustering_criteria

    Silhouette (clustering) SimHash; Similarity measure; Simple matching coefficient; V. Variation of information This page was last edited on 13 November 2023, at ...