listDerivableFeatureSpaces

Builds list of derivable feature spaces.

internal

Graphical toolbox for clustering and classification of data frames.
It proposes a graphical interface to process clustering and classification methods on features
data-frames, and to view initial data as well as resulted cluster or classes. According to the
level of available labels, different approaches are proposed: unsupervised clustering,
semi-supervised clustering and supervised classification.
To assess the processed clusters or classes, the toolbox can import and show some supplementary
data formats: either profile/time series, or images.
These added information can help the expert to label clusters (clustering), or to constrain data
frame rows (semi-supervised clustering), using Constrained spectral embedding algorithm by
Wacquet et al. (2013) <doi:10.1016/j.patrec.2013.02.003> and the methodology provided by
Wacquet et al. (2013) <doi:10.1007/978-3-642-35638-4_21>.

Pierre-Alexandre Hebert

RclusTool

Graphical Toolbox for Clustering and Classification of Data
Frames

Guillaume Wacquet

Emilie Poisson

Pierre Talon

listDerivableFeatureSpaces function

<dl><dt>scaling</dt>
<dd>boolean TRUE if scaling required.</dd>
<dt>pca</dt>
<dd>boolean TRUE if Principal Components Analysis required.</dd>
<dt>spectral</dt>
<dd>boolean TRUE if Spectral Embedding required.</dd>
<dt>RclusTool.env</dt>
<dd>environment in which all global parameters, raw data and results are stored.</dd></dl>

Arguments

Builds list of derivable feature spaces — listDerivableFeatureSpaces

<dl>

<dt>scaling</dt>
<dd>boolean TRUE if scaling required.</dd>


<dt>pca</dt>
<dd>boolean TRUE if Principal Components Analysis required.</dd>


<dt>spectral</dt>
<dd>boolean TRUE if Spectral Embedding required.</dd>


<dt>RclusTool.env</dt>
<dd>environment in which all global parameters, raw data and results are stored.</dd>

</dl>

listDerivableFeatureSpaces: Builds list of derivable feature spaces

Description

Usage

Value

Arguments

Details

Examples