R: external clustering validation

external_validation {ClusterR} | R Documentation

## external clustering validation

### Description

external clustering validation

### Usage

    external_validation(
      true_labels,
      clusters,
      method = "adjusted_rand_index",
      summary_stats = FALSE
    )

### Arguments

`true_labels` | a numeric vector of length equal to the length of the clusters vector
`clusters` | a numeric vector ( the result of a clustering method ) of length equal to the length of the true_labels
`method` | one of rand_index, adjusted_rand_index, jaccard_index, fowlkes_Mallows_index, mirkin_metric, purity, entropy, nmi (normalized mutual information), var_info (variation of information), and nvi (normalized variation of information)
`summary_stats` | besides the available methods the summary_stats parameter prints also the specificity, sensitivity, precision, recall and F-measure of the clusters

### Details

This function uses external validation methods to evaluate the clustering results

### Value

if summary_stats is FALSE the function returns a float number, otherwise it returns also a summary statistics table

### Author(s)

Lampros Mouselimis

### Examples

    data(dietary_survey_IBS)

    dat = dietary_survey_IBS[, -ncol(dietary_survey_IBS)]

    X = center_scale(dat)

    km = KMeans_rcpp(X, clusters = 2, num_init = 5, max_iters = 100, initializer = 'kmeans++')

    res = external_validation(dietary_survey_IBS$class, km$clusters, method = "adjusted_rand_index")

[Package ClusterR version 1.2.2 Index]