Clustering (version 1.7)

evaluate_best_validation_internal_by_metrics: Evaluation of the algorithms by measures of dissimilarity.

Description

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

Usage

evaluate_best_validation_internal_by_metrics(df)

Arguments

df

data matrix or data frame with the result of running the clustering algorithm.

Value

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

Details

Method that calculates the behavior of dissimilarity measures by algorithm, so we can evaluate which of the different measures of dissimilarity used by the algorithms presents the best behavior. This method should be used to determine which dissimilarity measure has the best behavior for intenal evaluation measures.

Examples

# NOT RUN {
result = clustering(df = cluster::agriculture, min = 4, max = 5, algorithm='gmm', attributes = TRUE)

evaluate_best_validation_internal_by_metrics(result)

result = clustering(
               df = cluster::agriculture,
               min = 4,
               max = 5,
               algorithm='gmm',
               metrics=c("connectivity"),
               attributes = TRUE
         )

evaluate_best_validation_internal_by_metrics(result)
# }