clustInd_hierarch {ehymet}

Perform hierarchical clustering for a different combinations of indices, method and distance

Description

Perform hierarchical clustering for a different combinations of indices, method and distance

Usage

clustInd_hierarch(
  ind_data,
  vars_combinations,
  method_list = c("single", "complete", "average", "centroid", "ward.D2"),
  dist_vector = c("euclidean", "manhattan"),
  n_cluster = 2,
  true_labels = NULL,
  n_cores = 1
)

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.

method_list

list of clustering methods.

dist_vector

list of distance metrics.

n_cluster

number of clusters to generate.

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.

Value

A list containing hierarchical clustering results for each configuration.

Examples

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