clustInd_spc

Perform spectral clustering for a different combinations of indices
and kernels

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

clustInd_spc function

<dl><dt>ind_data</dt>
<dd>Dataframe containing indices applied to the original data and
its first and second derivatives. See generate_indices.</dd>
<dt>vars_combinations</dt>
<dd><code>list</code> containing one or more combinations of indices in
<code>ind_data</code>. If it is non-named, the names of the variables are set to
vars1, ..., varsk, where k is the number of elements in <code>vars_combinations</code>.</dd>
<dt>kernel_list</dt>
<dd>List of kernels</dd>
<dt>n_cluster</dt>
<dd>Number of clusters to create</dd>
<dt>true_labels</dt>
<dd>Vector of true labels for validation
(if it is not known true_labels is set to NULL)</dd>
<dt>n_cores</dt>
<dd>Number of cores to do parallel computation. 1 by default,
which mean no parallel execution.</dd></dl>

Arguments

Spectral clustering using indices — clustInd_spc

<dl>

<dt>ind_data</dt>
<dd>Dataframe containing indices applied to the original data and
its first and second derivatives. See generate_indices.</dd>


<dt>vars_combinations</dt>
<dd><code>list</code> containing one or more combinations of indices in
<code>ind_data</code>. If it is non-named, the names of the variables are set to
vars1, ..., varsk, where k is the number of elements in <code>vars_combinations</code>.</dd>


<dt>kernel_list</dt>
<dd>List of kernels</dd>


<dt>n_cluster</dt>
<dd>Number of clusters to create</dd>


<dt>true_labels</dt>
<dd>Vector of true labels for validation
(if it is not known true_labels is set to NULL)</dd>


<dt>n_cores</dt>
<dd>Number of cores to do parallel computation. 1 by default,
which mean no parallel execution.</dd>

</dl>

clustInd_spc: Spectral clustering using indices

Description

Usage

Value

Arguments

Examples