Clara_Medoids {ClusterR} | R Documentation |
Clustering large applications
Clara_Medoids(
data,
clusters,
samples,
sample_size,
distance_metric = "euclidean",
minkowski_p = 1,
threads = 1,
swap_phase = TRUE,
fuzzy = FALSE,
verbose = FALSE,
seed = 1
)
data |
matrix or data frame |
clusters |
the number of clusters |
samples |
number of samples to draw from the data set |
sample_size |
fraction of data to draw in each sample iteration. It should be a float number greater than 0.0 and less or equal to 1.0 |
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. Openmp will be utilized to parallelize the number of the different sample draws |
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 |
integer value for random number generator (RNG) |
The Clara_Medoids function is implemented in the same way as the 'clara' (clustering large applications) algorithm (Kaufman and Rousseeuw(1990)). In the 'Clara_Medoids' the 'Cluster_Medoids' function will be applied to each sample draw.
a list with the following attributes : medoids, medoid_indices, sample_indices, best_dissimilarity, clusters, fuzzy_probs (if fuzzy = TRUE), clustering_stats, dissimilarity_matrix, silhouette_matrix
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)
clm = Clara_Medoids(dat, clusters = 3, samples = 5, sample_size = 0.2, swap_phase = TRUE)