evaluate_best_validation_internal_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_internal_by_metrics(df, metric)

Arguments

df	Data matrix or data frame with the result of running the clustering algorithm.
metric	It's a string with the metric to evaluate.

Details

This 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='gmm',
               metrics=c("Precision","Connectivity")
         )

Clustering::evaluate_best_validation_internal_by_metrics(result,"Connectivity")

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