Silhouette width based on pre-computed clusters

Silhouette width based on pre-computed clusters

silhouette_of_clusters(data, clusters)

Arguments

data

a matrix or a data frame

clusters

a numeric vector which corresponds to the pre-computed clusters (see the example section for more details). The size of the 'clusters' vector must be equal to the number of rows of the input data

Value

a list object where the first sublist is the 'silhouette summary', the second sublist is the 'silhouette matrix' and the third sublist is the 'global average silhouette' (based on the silhouette values of all observations)

Author

Lampros Mouselimis

Examples

data(dietary_survey_IBS)
dat = dietary_survey_IBS[, -ncol(dietary_survey_IBS)]
dat = center_scale(dat)

clusters = 2

# compute k-means
km = KMeans_rcpp(dat, clusters = clusters, num_init = 5, max_iters = 100, initializer = 'kmeans++')

# compute the silhouette width
silh_km = silhouette_of_clusters(data = dat, clusters = km$clusters)

# silhouette summary
silh_summary = silh_km$silhouette_summary

# silhouette matrix (including cluster & dissimilarity)
silh_mtrx = silh_km$silhouette_matrix

# global average silhouette
glob_avg = silh_km$silhouette_global_average