clustering_validation

Create a table containing four validation metrics for clustering: Purity, F-measure
and Rand Index (RI) and Adjusted Rand Index (ARI). This function considers pairs of points

Implements methods for functional data analysis based on the epigraph
and hypograph indices. These methods transform
functional datasets, whether in one or multiple dimensions, into multivariate
datasets. The transformation involves applying the epigraph, hypograph, and
their modified versions to both the original curves and their first and second
derivatives. The calculation of these indices is tailored to the dimensionality
of the functional dataset, with special considerations for dependencies between
dimensions in multidimensional cases. This approach extends traditional multivariate
data analysis techniques to the functional data setting. A key application of
this package is the EHyClus method, which enhances clustering analysis for
functional data across one or multiple dimensions using the epigraph and
hypograph indices. See Pulido et al. (2023) <doi:10.1007/s11222-023-10213-7>
and Pulido et al. (2024) <doi:10.48550/arXiv.2307.16720>.

Belen Pulido

ehymet

Methodologies for Functional Data Based on the Epigraph and
Hypograph Indices

Jose Ignacio Diez

clustering_validation function

<dl><dt>clusters</dt>
<dd>The clusters predicted by the clustering method.</dd>
<dt>true_labels</dt>
<dd>Atomic vector with the true labels of the data.</dd>
<dt>digits</dt>
<dd>Number of digits for rounding.</dd></dl>

Arguments

Create a table containing four validation metrics for clustering: Purity, F-measure
and Rand Index (RI) and Adjusted Rand Index (ARI). This function considers pairs of points — clustering_validation

<dl>

<dt>clusters</dt>
<dd>The clusters predicted by the clustering method.</dd>


<dt>true_labels</dt>
<dd>Atomic vector with the true labels of the data.</dd>


<dt>digits</dt>
<dd>Number of digits for rounding.</dd>

</dl>

Create a table containing four validation metrics for clustering: Purity, F-measure
and Rand Index (RI) and Adjusted Rand Index (ARI). This function considers pairs of points

clustering_validation: Create a table containing four validation metrics for clustering: Purity, F-measure and Rand Index (RI) and Adjusted Rand Index (ARI). This function considers pairs of points

Description

Usage

Value

Arguments

Examples