Calculates the approximate stability score (ST) of individual objects in a clustering solution (the approximate version allowing one to avoid possible variable overflow errors).

Description

This function will return the individual stability score ST and the global score STglobal using either the K-means or K-medoids algorithm and four different clustering indices: Calinski-Harabasz, Silhouette, Dunn or Davies-Bouldin.

Usage

1

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 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

1
2
3
   ## Calculates the stability scores of individual objects of the Iris dataset
   ## using K-means, 100 replicates (random starts) and k=3
   ClusterStability(dat=iris[1:4],k=3,replicate=100,type='kmeans');