clustInd_spc: Spectral clustering using indices

In ehymet: Methodologies for Functional Data Based on the Epigraph and Hypograph Indices

Spectral clustering using indices

Description

Perform spectral clustering for a different combinations of indices and kernels

Usage

clustInd_spc(
  ind_data,
  vars_combinations,
  kernel_list = c("rbfdot", "polydot"),
  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.
kernel_list	List of kernels
n_cluster	Number of clusters to create
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 kkmeans 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_spc(data_ind, list(vars1, vars2))

ehymet documentation built on June 22, 2024, 10:50 a.m.