ehymet (version 0.1.1)

clustInd_kmeans: K-means clustering with indices

Description

Perform k-means clustering for a different combinations of indices and distances.

Usage

clustInd_kmeans(
  ind_data,
  vars_combinations,
  dist_vector = c("euclidean", "mahalanobis"),
  n_cluster = 2,
  init = "random",
  true_labels = NULL,
  n_cores = 1
)

Value

A list containing hierarchical clustering results for each configuration

A list containing kmeans clustering results for each configuration

Arguments

ind_data

Dataframe containing indices applied to the original data and its first and second derivatives. See generate_indices.

vars_combinations

list containing one or more combinations of indices in ind_data. If it is non-named, the names of the variables are set to vars1, ..., varsk, where k is the number of elements in vars_combinations.

dist_vector

Atomic vector of distance metrics. The possible values are, "euclidean", "mahalanobis" or both.

n_cluster

Number of clusters to create.

init

Centroids initialization meathod. It can be "random" or "kmeanspp".

true_labels

Vector of true labels for validation. (if it is not known true_labels is set to NULL)

n_cores

Number of cores to do parallel computation. 1 by default, which mean no parallel execution.

Examples

vars1 <- c("dtaEI", "dtaMEI")
vars2 <- c("dtaHI", "dtaMHI")
data <- ehymet::sim_model_ex1()
data_ind <- generate_indices(data)
clustInd_kmeans(data_ind, list(vars1, vars2))