Method that evaluates clustering algorithm from a file directory or dataframe.
execute_package_parallel(
directory_files,
df,
algorithms_execute,
measures_execute,
cluster_min,
cluster_max,
metrics_execute,
attributes,
number_algorithms,
numberClusters,
numberDataSets,
is_metric_external,
is_metric_internal,
name_dataframe
)It's a string with the route where the datasets are located.
data matrix or data frame, or dissimilarity matrix, depending on the value of the argument.
character vector with the algorithms to be executed. The algorithms implemented are: fuzzy_cm,fuzzy_gg,fuzzy_gk,hclust, apclusterK,agnes,clara,daisy,diana,fanny,mona,pam,gmm,kmeans_arma, kmeans_rcpp,mini_kmeans,pvclust.
character array with the measurements of dissimilarity to be executed. Depending on the algorithm, one or the other is implemented. Among them we highlight: Euclidena, Manhattan, etc.
minimum number of clusters.
maximum number of clusters. cluster_max must be greater or equal cluster_min.
character array defining the metrics to be executed. The night metrics implemented are: entropy, variation_information,precision, recall,f_measure,fowlkes_mallows_index,connectivity,dunn,silhouette.
accepts Boolean values. If true as a result it shows the attribute that behaves best otherwise it shows the value of the executed metric.
It's a numeric field with the number of algorithms.
It's a numeric field with the difference between clusters.
It's a numeric field with the number of datasets.
boolean field to indicate whether to run external metrics.
boolean field to indicate whether to run internal metrics.
name of data.frame when is fill.
returns a list with the result matrix of evaluating the data from the indicated algorithms, metrics and number of clusters.