external clustering validation
external_validation(
true_labels,
clusters,
method = "adjusted_rand_index",
summary_stats = FALSE
)a numeric vector of length equal to the length of the clusters vector
a numeric vector ( the result of a clustering method ) of length equal to the length of the true_labels
one of rand_index, adjusted_rand_index, jaccard_index, fowlkes_Mallows_index, mirkin_metric, purity, entropy, nmi (normalized mutual information), var_info (variation of information), and nvi (normalized variation of information)
besides the available methods the summary_stats parameter prints also the specificity, sensitivity, precision, recall and F-measure of the clusters
if summary_stats is FALSE the function returns a float number, otherwise it returns also a summary statistics table
This function uses external validation methods to evaluate the clustering results
data(dietary_survey_IBS)
dat = dietary_survey_IBS[, -ncol(dietary_survey_IBS)]
X = center_scale(dat)
km = KMeans_rcpp(X, clusters = 2, num_init = 5, max_iters = 100, initializer = 'kmeans++')
res = external_validation(dietary_survey_IBS$class, km$clusters, method = "adjusted_rand_index")