clustInd_kmeans {ehymet} | R Documentation |
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,
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 |
|
dist_vector |
Atomic vector of distance metrics. The possible values are, "euclidean", "mahalanobis" or both. |
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 hierarchical clustering results for each configuration
A list containing kmeans 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_kmeans(data_ind, list(vars1, vars2))