cost_clusters_from_dissim_medoids: Compute the cost and clusters based on an input dissimilarity...

In ClusterR: Gaussian Mixture Models, K-Means, Mini-Batch-Kmeans, K-Medoids and Affinity Propagation Clustering

Compute the cost and clusters based on an input dissimilarity matrix and medoids

Description

Compute the cost and clusters based on an input dissimilarity matrix and medoids

Usage

cost_clusters_from_dissim_medoids(data, medoids)

Arguments

data	a dissimilarity matrix, where the main diagonal equals 0.0 and the number of rows equals the number of columns
medoids	a vector of output medoids of the 'Cluster_Medoids', 'Clara_Medoids' or any other 'partition around medoids' function

Value

a list object that includes the cost and the clusters

Author(s)

Lampros Mouselimis

Examples

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

cm = Cluster_Medoids(dat, clusters = 3, distance_metric = 'euclidean', swap_phase = TRUE)
res = cost_clusters_from_dissim_medoids(data = cm$dissimilarity_matrix, medoids = cm$medoid_indices)

# cm$best_dissimilarity == res$cost
# table(cm$clusters, res$clusters)

ClusterR documentation built on April 30, 2023, 1:08 a.m.