sub-.clustering: Filter metrics in a 'clustering' object returning a new...

In laperez/Clustering: Techniques for Evaluating Clustering

Filter metrics in a clustering object returning a new clustering object.

Description

Generates a new filtered clustering object.

Usage

## S3 method for class 'clustering'
clustering[condition = TRUE]

Arguments

clustering	The clustering object to filter.
condition	Expression to filter the clustering object.

Details

This function allows you to filter the data set for a given evaluation metric. The evaluation metrics available are: Algorithm, Distance, Clusters, Data, Var, Time, Entropy, Variation_information, Precision, Recall, F_measure, Fowlkes_mallows_index, Connectivity, Dunn, Silhouette and TimeAtt.

Value

A clustering object filtered from the input parameters.

Examples

result <- Clustering::clustering(df = Clustering::basketball, algorithm = 'clara',
min=3, max=4, metrics = c('Precision','Recall'))

result[Precision > 0.14 & Recall > 0.11]

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