tglkmeans (version 0.5.4)

TGL_kmeans: kmeans++ with return value similar to R kmeans

Description

kmeans++ with return value similar to R kmeans

Usage

TGL_kmeans(
  df,
  k,
  metric = "euclid",
  max_iter = 40,
  min_delta = 0.0001,
  verbose = FALSE,
  keep_log = FALSE,
  id_column = FALSE,
  reorder_func = "hclust",
  hclust_intra_clusters = FALSE,
  seed = NULL,
  parallel = getOption("tglkmeans.parallel"),
  use_cpp_random = FALSE
)

Value

list with the following components:

cluster:

A vector of integers (from ‘1:k’) indicating the cluster to which each point is allocated.

centers:

A matrix of cluster centers.

size:

The number of points in each cluster.

log:

messages from the algorithm run (only if id_column == TRUE).

order:

A vector of integers with the new ordering if the observations. (only if hclust_intra_clusters = TRUE)

Arguments

df

a data frame or a matrix. Each row is a single observation and each column is a dimension. the first column can contain id for each observation (if id_column is TRUE), otherwise the rownames are used.

k

number of clusters. Note that in some cases the algorithm might return less clusters than k.

metric

distance metric for kmeans++ seeding. can be 'euclid', 'pearson' or 'spearman'

max_iter

maximal number of iterations

min_delta

minimal change in assignments (fraction out of all observations) to continue iterating

verbose

display algorithm messages

keep_log

keep algorithm messages in 'log' field

id_column

df's first column contains the observation id

reorder_func

function to reorder the clusters. operates on each center and orders by the result. e.g. reorder_func = mean would calculate the mean of each center and then would reorder the clusters accordingly. If reorder_func = hclust the centers would be ordered by hclust of the euclidean distance of the correlation matrix, i.e. hclust(dist(cor(t(centers)))) if NULL, no reordering would be done.

hclust_intra_clusters

run hierarchical clustering within each cluster and return an ordering of the observations.

seed

seed for the c++ random number generator

parallel

cluster every cluster parallelly (if hclust_intra_clusters is true)

use_cpp_random

use c++ random number generator instead of R's. This should be used for only for backwards compatibility, as from version 0.4.0 onwards the default random number generator was changed o R.

See Also

TGL_kmeans_tidy

Examples

# \dontshow{
# this line is only for CRAN checks
tglkmeans.set_parallel(1)
# }

# create 5 clusters normally distributed around 1:5
d <- simulate_data(
    n = 100,
    sd = 0.3,
    nclust = 5,
    dims = 2,
    add_true_clust = FALSE,
    id_column = FALSE
)

head(d)

# cluster
km <- TGL_kmeans(d, k = 5, "euclid", verbose = TRUE)
names(km)
km$centers
head(km$cluster)
km$size