silhouette_of_clusters function

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

Returns

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)

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

Author(s)

Lampros Mouselimis