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RclusTool (version 0.91.6)

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

0.91.6

License

GPL (>= 2)

Maintainer

Pierre-Alexandre Hebert

Last Published

February 27th, 2024

Functions in RclusTool (0.91.6)

computeCKmeans

Constrained K-means clustering
computeCSC

Constrained Spectral Clustering
computeGap2

Gap computation
buildsupTab

Supervised tab
buildUnsupTab

Unsupervised tab
computeGaussianSimilarity

Gaussian similarity
computeSupervised

Supervised classification
computeGap

Gap computation
computeUnSupervised

Unsupervised clustering
computeSpectralEmbeddingSample

Spectral embedding
computeGaussianSimilarityZP

Gaussian similarity
convNamesPairsToIndexPairs

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

Expectation-Maximization clustering
buildSemisupTab

Semi-Supervised tab
buildPreprocessTab

build Preprocess tab
countItemsSampleGUI

GUI to manually count the number of cells in colonies
computeItemsSample

Prediction of number of cells in colonies
applyPreprocessing

Preprocessing application
convNamesToIndex

Conversion of element names to indexes
.logoFrame

Logo frame in the graphical user interface
detailOperation

detail Operation
countItems

Manually counting the number of cells in colonies
clusterDensity

Clusters density computation
imgClassif

Images clustering
cor.mtest

Correlation test.
guessFileEncoding

File Encoding Identification.
buildImportTab

Build Import tab
dropTrainSetVars

Parameters dropping
importLabelSample

Labels importation
computePcaSample

Principal Components Analysis
clusterSummary

Clusters summaries computation
computeSemiSupervised

Semi-supervised clustering
computeSampling

Sampling raw data matrix
loadPreviousRes

Previous clustering results loading
computePcaNbDims

Number of dimensions for PCA
loadSample

Sample loading
extractProtos

Prototypes extraction
featSpaceNameConvert

Feature Space Name Conversion
initSemisupTab

Semi-Supervised tab
initImportTab

import tab
itemsModel

Predictive models computation for the number of cells in colonies
buildNameOperation

Build Name Operation
computeItemsSampleGUI

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

Rates of constraints satisfaction
computeKmeans

K-means clustering
initBatchTab

batch tab
extractFeaturesFromSummary

Extraction of features from a summary object.
initUnsupTab

Unsupervised tab
formatLabelSample

Labels formatting
saveSummary

Clusters summaries saving
search.neighbour

Search neighbour
initSupTab

supervised tab
formatParameterList

Format Parameter List
importSample

Sample importation
saveClustering

Clustering saving
initParameters

Parameters initialization
critMNCut

Multiple Normalized Cut
createResFolder

Results directories creation
readTrainSet

Training set reading
saveCalcul

Object saving
makeTitle

RclusTool makeTitle.
plotProfileExtract

Profile and image plotting
removeZeros

Zeros replacement
sortLabel

Clusters labels sorting
matchNames

Match Names
plotSampleFeatures

2D-features scatter-plot
plotProfile

Profile and image plotting
spectralClusteringNg

Spectral clustering
saveCounts

Count saving
saveLogFile

Log file saving
listDerivableFeatureSpaces

Builds list of derivable feature spaces
loadSummary

Summaries loading
plotDensity2D

plot Variables Density
makeFeatureSpaceOperations

Make operation config object to build feature spaces
nameClusters

Clusters renaming
initPreprocessTab

build Preprocess tab
tk2add.notetab

Add notetab.
messageConsole

RclusTool consoleMessage.
measureMNCut

Multiple Normalized Cut
saveManualProtos

Manual prototypes saving
savePreprocess

Preprocessing exportation
spectralClustering

Spectral clustering
visualizeSampleClustering

Interactive figure with 2D scatter-plot
updateClustersNames

Clusters names updating
spectralEmbeddingNg

Spectral embedding
purgeSample

Sample purging
loadPreprocessFile

Preprocessing loading
previewCSVfile

Preview CSV file
tkrplot.RclusTool

RclusTool tkrplot.
tkEmptyLine

RclusTool tkEmptyLine.
tk2draw.notetab

Draw in a Notetab.
tk2notetab.RclusTool

RclusTool tk2notetab.
sortCharAsNum

Character vector numeric sorting
sigClassif

Signals clustering
tkrreplot.RclusTool

RclusTool tkrreplot.
toStringDataFrame

To String Data Frame
tk2delete.notetab

Delete notetab inside a tk-notebook
KwaySSSC

Semi-supervised spectral clustering
RclusToolGUI

Username and user type selection
abdPlotTabs

Abundances barplots inside Tk tabs.
KmeansAutoElbow

Kmeans clustering with automatic estimation of number of clusters
ElbowPlot

Elbow Plot.
FindNumberK

Automatic estimation of the number of clusters
MainWindow

Main window
abdPlot

Abundances barplot
analyzePlot

Plot for data exploration/analysis
ElbowFinder

Elbow Finder
KmeansQuick

Quick kmeans clustering
bipartitionShi

Spectral clustering
abdPlotTabsGUI

Abundances barplots inside Tk tabs.
addClustering

Clustering addition
buildClusteringSample

Clustering loading
addIds2Sampling

Adding Ids To a Sampling
buildConstraintsMatrix

Constraints matrices
buildBatchTab

Batch process tab
addOperation

Add operation