execute_package_parallel function

Evaluation clustering algorithm.

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
)

Arguments

• directory_files: It's a string with the route where the datasets are located.
• df: Data matrix or data frame, or dissimilarity matrix, depending on the value of the argument.
• algorithms_execute: Character vector with the algorithms to be executed. The algorithms implemented are: hclust, apclusterK,agnes,clara,daisy,diana,fanny,mona,pam,gmm,kmeans_arma, kmeans_rcpp,mini_kmeans, pvclust.
• measures_execute: 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.
• cluster_min: Minimum number of clusters.
• cluster_max: Maximum number of clusters. cluster_max must be greater or equal cluster_min.
• metrics_execute: 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 and Silhouette.
• number_algorithms: It's a numeric field with the number of algorithms.
• numberClusters: It's a numeric field with the difference between clusters.
• numberDataSets: It's a numeric field with the number of datasets.
• is_metric_external: Boolean field to indicate whether to run external metrics.
• is_metric_internal: Boolean field to indicate whether to run internal metrics.
• name_dataframe: Name of data.frame when is fill.

Returns

Returns a list with the result matrix of evaluating the data from the indicated algorithms, metrics and number of clusters.