listDerivableFeatureSpaces

scaling

boolean TRUE if Principal Components Analysis required.

boolean TRUE if Spectral Embedding required.

spectral

environment in which all global parameters, raw data and results are stored.

RclusTool.env

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>.

listDerivableFeatureSpaces: Builds list of derivable feature spaces

Description

Usage

Arguments

Value

Details

Examples