Optimal_Clusters_Medoids: Optimal number of Clusters for the partitioning around...

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

Optimal number of Clusters for the partitioning around Medoids functions

Description

Optimal number of Clusters for the partitioning around Medoids functions

Usage

Optimal_Clusters_Medoids(
  data,
  max_clusters,
  distance_metric,
  criterion = "dissimilarity",
  clara_samples = 0,
  clara_sample_size = 0,
  minkowski_p = 1,
  swap_phase = TRUE,
  threads = 1,
  verbose = FALSE,
  plot_clusters = TRUE,
  seed = 1
)

Arguments

data	matrix or data.frame. If both clara_samples and clara_sample_size equal 0, then 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
max_clusters	either a numeric value, a contiguous or non-continguous numeric vector specifying the cluster search space
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
criterion	one of 'dissimilarity' or 'silhouette'
clara_samples	number of samples to draw from the data set in case of clustering large applications (clara)
clara_sample_size	fraction of data to draw in each sample iteration in case of clustering large applications (clara). It should be a float number greater than 0.0 and less or equal to 1.0
minkowski_p	a numeric value specifying the minkowski parameter in case that distance_metric = "minkowski"
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.
threads	an integer specifying the number of cores to run in parallel. Openmp will be utilized to parallelize the number of sample draws
verbose	either TRUE or FALSE, indicating whether progress is printed during clustering
plot_clusters	TRUE or FALSE, indicating whether the iterative results should be plotted. See the details section for more information
seed	integer value for random number generator (RNG)

Details

In case of plot_clusters = TRUE, the first plot will be either a plot of dissimilarities or both dissimilarities and silhouette widths giving an indication of the optimal number of the clusters. Then, the user will be asked to give an optimal value for the number of the clusters and after that the second plot will appear with either the dissimilarities or the silhouette widths belonging to each cluster.

In case that the max_clusters parameter is a contiguous or non-contiguous vector then plotting is disabled. Therefore, plotting is enabled only if the max_clusters parameter is of length 1.

Value

a list of length equal to the max_clusters parameter (the first sublist equals NULL, as dissimilarities and silhouette widths can be calculated if the number of clusters > 1). If plot_clusters is TRUE then the function plots also the results.

Author(s)

Lampros Mouselimis

Examples

## Not run: 
data(soybean)

dat = soybean[, -ncol(soybean)]

opt_md = Optimal_Clusters_Medoids(dat, 10, 'jaccard_coefficient', plot_clusters = FALSE)


#----------------------------
# non-contiguous search space
#----------------------------

search_space = c(2,5)

opt_md = Optimal_Clusters_Medoids(dat, search_space, 'jaccard_coefficient', plot_clusters = FALSE)


## End(Not run)

ClusterR documentation built on June 22, 2024, 10:28 a.m.