Assessment of the stability of individual objects, clusters and a whole clustering solution based on repeated runs of a clustering algorithm.
The ClusterStability package uses a probabilistic framework and some well-known clustering criteria (e.g. Calinski-Harabasz, Silhouette, Dunn and Davies-Bouldin) to compute the stability scores (ST) of each individual object (i.e., element) in the clustering solution provided by the K-means and K-medoids partitioning algorithms.
|Maintainer:||Etienne Lord <email@example.com>,|
|Vladimir Makarenkov <firstname.lastname@example.org>|
ClusterStability computes the individual and global stability scores (ST) for a partitioning solution using either K-means or K-medoids (the approximate solution is provided).
ClusterStability_exact is similar to the
ClusterStability function but uses the Stirling numbers of the second kind to compute the exact stability scores (but is limited to a small number of objects).
Kcombination computes the k-combination of a set of numbers for a given k.
Reorder returns the re-ordered partitioning of a series of clusters.
Stirling2nd computes the Stirling numbers of the second kind.
Etienne Lord, François-Joseph Lapointe and Vladimir Makarenkov
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