evaluate_best_validation_internal_by_metrics function

Evaluates algorithms by measures of dissimilarity based on a metric.

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

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.

Returns

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

Details

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

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")