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

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evaluate_best_validation_internal_by_metricsR Documentation

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(
               df = cluster::agriculture,
               min = 4,
               max = 5,
               algorithm='gmm',
               metrics=c("Precision","Connectivity")
         )

evaluate_best_validation_internal_by_metrics(result,"Connectivity")


Clustering documentation built on June 22, 2022, 5:07 p.m.