Optimal_Clusters_KMeans {ClusterR}R Documentation

Optimal number of Clusters for Kmeans or Mini-Batch-Kmeans

Description

Optimal number of Clusters for Kmeans or Mini-Batch-Kmeans

Usage

Optimal_Clusters_KMeans(
  data,
  max_clusters,
  criterion = "variance_explained",
  fK_threshold = 0.85,
  num_init = 1,
  max_iters = 200,
  initializer = "kmeans++",
  tol = 1e-04,
  plot_clusters = TRUE,
  verbose = FALSE,
  tol_optimal_init = 0.3,
  seed = 1,
  mini_batch_params = NULL
)

Arguments

data

matrix or data frame

max_clusters

either a numeric value, a contiguous or non-continguous numeric vector specifying the cluster search space

criterion

one of variance_explained, WCSSE, dissimilarity, silhouette, distortion_fK, AIC, BIC and Adjusted_Rsquared. See details for more information.

fK_threshold

a float number used in the 'distortion_fK' criterion

num_init

number of times the algorithm will be run with different centroid seeds

max_iters

the maximum number of clustering iterations

initializer

the method of initialization. One of, optimal_init, quantile_init, kmeans++ and random. See details for more information

tol

a float number. If, in case of an iteration (iteration > 1 and iteration < max_iters) 'tol' is greater than the squared norm of the centroids, then kmeans has converged

plot_clusters

either TRUE or FALSE, indicating whether the results of the Optimal_Clusters_KMeans function should be plotted

verbose

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

tol_optimal_init

tolerance value for the 'optimal_init' initializer. The higher this value is, the far appart from each other the centroids are.

seed

integer value for random number generator (RNG)

mini_batch_params

either NULL or a list of the following parameters : batch_size, init_fraction, early_stop_iter. If not NULL then the optimal number of clusters will be found based on the Mini-Batch-Kmeans. See the details and examples sections for more information.

Details

—————criteria————————–

variance_explained : the sum of the within-cluster-sum-of-squares-of-all-clusters divided by the total sum of squares

WCSSE : the sum of the within-cluster-sum-of-squares-of-all-clusters

dissimilarity : the average intra-cluster-dissimilarity of all clusters (the distance metric defaults to euclidean)

silhouette : the average silhouette width of all clusters (the distance metric defaults to euclidean)

distortion_fK : this criterion is based on the following paper, 'Selection of K in K-means clustering' (https://www.ee.columbia.edu/~dpwe/papers/PhamDN05-kmeans.pdf)

AIC : the Akaike information criterion

BIC : the Bayesian information criterion

Adjusted_Rsquared : the adjusted R^2 statistic

—————initializers———————-

optimal_init : this initializer adds rows of the data incrementally, while checking that they do not already exist in the centroid-matrix [ experimental ]

quantile_init : initialization of centroids by using the cummulative distance between observations and by removing potential duplicates [ experimental ]

kmeans++ : kmeans++ initialization. Reference : http://theory.stanford.edu/~sergei/papers/kMeansPP-soda.pdf AND http://stackoverflow.com/questions/5466323/how-exactly-does-k-means-work

random : random selection of data rows as initial centroids

If the mini_batch_params parameter is not NULL then the optimal number of clusters will be found based on the Mini-batch-Kmeans algorithm, otherwise based on the Kmeans. The higher the init_fraction parameter is the more close the results between Mini-Batch-Kmeans and Kmeans will be.

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. Moreover, the distortion_fK criterion can't be computed if the max_clusters parameter is a contiguous or non-continguous vector ( the distortion_fK criterion requires consecutive clusters ). The same applies also to the Adjusted_Rsquared criterion which returns incorrect output.

Value

a vector with the results for the specified criterion. If plot_clusters is TRUE then it plots also the results.

Author(s)

Lampros Mouselimis

References

https://core.ac.uk/download/pdf/205410925.pdf

Examples


data(dietary_survey_IBS)

dat = dietary_survey_IBS[, -ncol(dietary_survey_IBS)]

dat = center_scale(dat)


#-------
# kmeans
#-------

opt_km = Optimal_Clusters_KMeans(dat, max_clusters = 10, criterion = "distortion_fK",

                                 plot_clusters = FALSE)

#------------------
# mini-batch-kmeans
#------------------


params_mbkm = list(batch_size = 10, init_fraction = 0.3, early_stop_iter = 10)

opt_mbkm = Optimal_Clusters_KMeans(dat, max_clusters = 10, criterion = "distortion_fK",

                                   plot_clusters = FALSE, mini_batch_params = params_mbkm)


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

search_space = c(2,5)

opt_km = Optimal_Clusters_KMeans(dat, max_clusters = search_space,

                                 criterion = "variance_explained",

                                 plot_clusters = FALSE)


[Package ClusterR version 1.2.5 Index]