ClusterR (version 1.3.3)

Cluster_Medoids: Partitioning around medoids

Description

Partitioning around medoids

Usage

Cluster_Medoids(
  data,
  clusters,
  distance_metric = "euclidean",
  minkowski_p = 1,
  threads = 1,
  swap_phase = TRUE,
  fuzzy = FALSE,
  verbose = FALSE,
  seed = 1
)

Value

a list with the following attributes: medoids, medoid_indices, best_dissimilarity, dissimilarity_matrix, clusters, fuzzy_probs (if fuzzy = TRUE), silhouette_matrix, clustering_stats

Arguments

data

matrix or data frame. The data parameter can be also a dissimilarity matrix, where the main diagonal equals 0.0 and the number of rows equals the number of columns

clusters

the number of clusters

distance_metric

a string specifying the distance method. One of, euclidean, manhattan, chebyshev, canberra, braycurtis, pearson_correlation, simple_matching_coefficient, minkowski, hamming, jaccard_coefficient, Rao_coefficient, mahalanobis, cosine

minkowski_p

a numeric value specifying the minkowski parameter in case that distance_metric = "minkowski"

threads

an integer specifying the number of cores to run in parallel

swap_phase

either TRUE or FALSE. If TRUE then both phases ('build' and 'swap') will take place. The 'swap_phase' is considered more computationally intensive.

fuzzy

either TRUE or FALSE. If TRUE, then probabilities for each cluster will be returned based on the distance between observations and medoids

verbose

either TRUE or FALSE, indicating whether progress is printed during clustering

seed

`r lifecycle::badge("deprecated")` `seed` (integer value for random number generator (RNG)) is no longer supported and will be removed in version 1.4.0

Author

Lampros Mouselimis

Details

Due to the fact that I didn't have access to the book 'Finding Groups in Data, Kaufman and Rousseeuw, 1990' (which includes the exact algorithm) I implemented the 'Cluster_Medoids' function based on the paper 'Clustering in an Object-Oriented Environment' (see 'References'). Therefore, the 'Cluster_Medoids' function is an approximate implementation and not an exact one. Furthermore, in comparison to k-means clustering, the function 'Cluster_Medoids' is more robust, because it minimizes the sum of unsquared dissimilarities. Moreover, it doesn't need initial guesses for the cluster centers.

References

Anja Struyf, Mia Hubert, Peter J. Rousseeuw, (Feb. 1997), Clustering in an Object-Oriented Environment, Journal of Statistical Software, Vol 1, Issue 4

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)