execute_datasets: Evaluation clustering algorithm.

In laperez/Clustering: Techniques for Evaluating Clustering

Evaluation clustering algorithm.

Description

Method of performing information processing

Usage

execute_datasets(
  path,
  df,
  packages,
  algorithm,
  cluster_min,
  cluster_max,
  metrics,
  attributes,
  name_dataframe
)

Arguments

path	Path where the datasets are located.
df	Data matrix or data frame, or dissimilarity matrix, depending on the value of the argument.
packages	Array defining the clustering package. The seven packages implemented are: cluster, ClusterR, amap, apcluster, pvclust. By default runs all packages.
algorithm	Array with the algorithms that implement the package. The algorithms implemented are: hclust,apclusterK, agnes,clara,daisy,diana,fanny,mona,pam,gmm,kmeans_arma,kmeans_rcpp, mini_kmeans, pvclust.
cluster_min	Minimum number of clusters. at least one must be.
cluster_max	Maximum number of clusters. cluster_max must be greater or equal cluster_min.
metrics	Array defining the metrics avalaible in the package. The night metrics implemented are: Entropy, Variation_information, Precision, Recall, F_measure, Fowlkes_mallows_index, Connectivity, Dunn and Silhouette.
name_dataframe	Name of data.frame when df is fill.

Value

Returns a matrix with the result of running all the metrics of the algorithms contained in the packages we indicated.

laperez/Clustering documentation built on June 25, 2022, 5:48 p.m.