makeFeatureSpaceOperations

boolean: if TRUE, Principal Components Analysis is applied to reduce the data space.

number of principal components kept. If pca.nb.dims=0, this number is computed automatically.

pca.nb.dims

boolean: if TRUE, spectral embedding is applied to reduce the data space.

spectral

boolean: if TRUE, data sampling is used.

sampling

: maximum size of the sampled dataframe.

sampling.size.max

boolean: if TRUE, scaling is applied.

scaling

makeFeatureSpaceOperations create config object for datasample

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>.

makeFeatureSpaceOperations: Make operation config object to build feature spaces

Description

Usage

Arguments

Examples