Clara_Medoids {ClusterR}

Clustering large applications

Description

Clustering large applications

Usage

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

Arguments

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)

Details

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.

Value

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

Author(s)

Lampros Mouselimis

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)

clm = Clara_Medoids(dat, clusters = 3, samples = 5, sample_size = 0.2, swap_phase = TRUE)