ClusterStability_exact {ClusterStability}

Calculates the exact stability score (ST) for individual objects in a clustering solution.

Description

This function will return the exact individual stability score ST and the exact global score STglobal using either the K-means or K-medoids algorithm and four different clustering indices: Calinski-Harabasz, Silhouette, Dunn or Davies-Bouldin. Variable overflow errors are possible for large numbers of objects.

Usage

ClusterStability_exact(dat, k, replicate, type) 

Arguments

dat

the input dataset: either a matrix or a dataframe.

k

the number of classes for the K-means or K-medoids algorithm (default=3).

replicate

the number of replicates to perform (default=1000).

type

the algorithm used in the partitioning: either 'kmeans' or 'kmedoids' algorithm (default=kmeans).

Value

Returns the exact individual (ST) and global (ST_global) stability scores for the four clustering indices: Calinski-Harabasz (ch), Silhouette (sil), Dunn (dunn) or Davies-Bouldin (db).

Examples

   ## Calculate the stability scores of individual objects of the Iris dataset
   ## using K-means, 100 replicates (random starts) and k=3
   ClusterStability_exact(dat=iris[1:4],k=3,replicate=100,type='kmeans');