evaluate_best_validation_external_by_metrics: Evaluates algorithms by measures of dissimilarity based on a...

In laperez/Clustering: Techniques for Evaluating Clustering

Evaluates algorithms by measures of dissimilarity based on a metric.

Description

Method that calculates which algorithm and which metric behaves best for the datasets provided.

Usage

evaluate_best_validation_external_by_metrics(df, metric)

Arguments

df	Data matrix or data frame with the result of running the clustering algorithm.
metric	String with the metric.

Details

Method groups the data by algorithm and distance measure, instead of obtaining the best attribute from the data set.

Value

A data.frame with the algorithms classified by measures of dissimilarity.

Examples

result = Clustering::clustering(
               df = cluster::agriculture,
               min = 4,
               max = 5,
               algorithm='kmeans_rcpp',
               metrics=c("F_measure"))

Clustering::evaluate_best_validation_external_by_metrics(result,'F_measure')

laperez/Clustering documentation built on April 23, 2024, 3:41 a.m.