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RclusTool (version 0.91.61)

Graphical Toolbox for Clustering and Classification of Data Frames

Description

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) and the methodology provided by Wacquet et al. (2013) .

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Version

Install

install.packages('RclusTool')

Monthly Downloads

245

Version

0.91.61

License

GPL (>= 2)

Maintainer

Pierre-Alexandre Hebert

Last Published

May 13th, 2025

Functions in RclusTool (0.91.61)

analyzePlot

Plot for data exploration/analysis
buildBatchTab

Batch process tab
buildUnsupTab

Unsupervised tab
buildsupTab

Supervised tab
bipartitionShi

Spectral clustering
buildNameOperation

Build Name Operation
buildPreprocessTab

build Preprocess tab
addClustering

Clustering addition
buildSemisupTab

Semi-Supervised tab
computeEM

Expectation-Maximization clustering
computeGap

Gap computation
applyPreprocessing

Preprocessing application
computeCKmeans

Constrained K-means clustering
computeSemiSupervised

Semi-supervised clustering
buildImportTab

Build Import tab
computeSpectralEmbeddingSample

Spectral embedding
computeGaussianSimilarity

Gaussian similarity
computeSampling

Sampling raw data matrix
computeCSC

Constrained Spectral Clustering
abdPlotTabsGUI

Abundances barplots inside Tk tabs.
extractProtos

Prototypes extraction
computeSupervised

Supervised classification
computeGap2

Gap computation
computeItemsSampleGUI

GUI to estimate the number of cells in colonies for each cluster
countItems

Manually counting the number of cells in colonies
countItemsSampleGUI

GUI to manually count the number of cells in colonies
dropTrainSetVars

Parameters dropping
.logoFrame

Logo frame in the graphical user interface
initSemisupTab

Semi-Supervised tab
featSpaceNameConvert

Feature Space Name Conversion
computeGaussianSimilarityZP

Gaussian similarity
detailOperation

detail Operation
computeItemsSample

Prediction of number of cells in colonies
convNamesToIndex

Conversion of element names to indexes
createResFolder

Results directories creation
extractFeaturesFromSummary

Extraction of features from a summary object.
initSupTab

supervised tab
guessFileEncoding

File Encoding Identification.
loadPreprocessFile

Preprocessing loading
critMNCut

Multiple Normalized Cut
plotDensity2D

plot Variables Density
imgClassif

Images clustering
messageConsole

RclusTool consoleMessage.
cor.mtest

Correlation test.
listDerivableFeatureSpaces

Builds list of derivable feature spaces
computeKmeans

K-means clustering
importLabelSample

Labels importation
initUnsupTab

Unsupervised tab
saveManualProtos

Manual prototypes saving
sigClassif

Signals clustering
savePreprocess

Preprocessing exportation
sortCharAsNum

Character vector numeric sorting
plotProfile

Profile and image plotting
toStringDataFrame

To String Data Frame
addIds2Sampling

Adding Ids To a Sampling
tkrreplot.RclusTool

RclusTool tkrreplot.
formatLabelSample

Labels formatting
nameClusters

Clusters renaming
initImportTab

import tab
formatParameterList

Format Parameter List
initBatchTab

batch tab
readTrainSet

Training set reading
clusterSummary

Clusters summaries computation
addOperation

Add operation
itemsModel

Predictive models computation for the number of cells in colonies
plotProfileExtract

Profile and image plotting
clusterDensity

Clusters density computation
plotSampleFeatures

2D-features scatter-plot
importSample

Sample importation
matchNames

Match Names
makeTitle

RclusTool makeTitle.
saveSummary

Clusters summaries saving
measureMNCut

Multiple Normalized Cut
sortLabel

Clusters labels sorting
loadSummary

Summaries loading
spectralEmbeddingNg

Spectral embedding
measureConstraintsOk

Rates of constraints satisfaction
removeZeros

Zeros replacement
makeFeatureSpaceOperations

Make operation config object to build feature spaces
search.neighbour

Search neighbour
spectralClusteringNg

Spectral clustering
tk2add.notetab

Add notetab.
previewCSVfile

Preview CSV file
computePcaSample

Principal Components Analysis
computePcaNbDims

Number of dimensions for PCA
tk2delete.notetab

Delete notetab inside a tk-notebook
computeUnSupervised

Unsupervised clustering
purgeSample

Sample purging
saveCalcul

Object saving
convNamesPairsToIndexPairs

Conversion of a set of names pairs to matrix of index pairs (2 columns)
spectralClustering

Spectral clustering
initParameters

Parameters initialization
saveCounts

Count saving
tkrplot.RclusTool

RclusTool tkrplot.
loadPreviousRes

Previous clustering results loading
loadSample

Sample loading
initPreprocessTab

build Preprocess tab
saveLogFile

Log file saving
tkEmptyLine

RclusTool tkEmptyLine.
saveClustering

Clustering saving
tk2draw.notetab

Draw in a Notetab.
updateClustersNames

Clusters names updating
visualizeSampleClustering

Interactive figure with 2D scatter-plot
tk2notetab.RclusTool

RclusTool tk2notetab.
abdPlot

Abundances barplot
abdPlotTabs

Abundances barplots inside Tk tabs.
buildConstraintsMatrix

Constraints matrices
buildClusteringSample

Clustering loading
KmeansQuick

Quick kmeans clustering
FindNumberK

Automatic estimation of the number of clusters
KmeansAutoElbow

Kmeans clustering with automatic estimation of number of clusters
ElbowFinder

Elbow Finder
ElbowPlot

Elbow Plot.
KwaySSSC

Semi-supervised spectral clustering
RclusToolGUI

Username and user type selection
MainWindow

Main window