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  2. Silhouette (clustering) - Wikipedia

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

    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 curse of dimensionality, as the distances become more similar. [2]

  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. Cluster analysis - Wikipedia

    en.wikipedia.org/wiki/Cluster_analysis

    Cluster analysis or clustering is the task of grouping a set of ... one could cluster the data set by the Silhouette coefficient; except that there is no known ...

  5. 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.

  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. Calinski–Harabasz index - Wikipedia

    en.wikipedia.org/wiki/Calinski–Harabasz_index

    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.

  8. Sen. Blumenthal says mysterious drones spotted recently ... - AOL

    www.aol.com/sen-blumenthal-says-mysterious...

    Sen. Richard Blumenthal, D-Conn., said the mysterious drones spotted in New Jersey over the past few weeks, and most recently in Connecticut, should be “shot down, if necessary."

  9. 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.