Cluster_Medoids {ClusterR} | R Documentation |
Partitioning around medoids
Cluster_Medoids(
data,
clusters,
distance_metric = "euclidean",
minkowski_p = 1,
threads = 1,
swap_phase = TRUE,
fuzzy = FALSE,
verbose = FALSE,
seed = 1
)
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 |
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.
a list with the following attributes: medoids, medoid_indices, best_dissimilarity, dissimilarity_matrix, clusters, fuzzy_probs (if fuzzy = TRUE), silhouette_matrix, clustering_stats
Lampros Mouselimis
Anja Struyf, Mia Hubert, Peter J. Rousseeuw, (Feb. 1997), Clustering in an Object-Oriented Environment, Journal of Statistical Software, Vol 1, Issue 4
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)