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RclusTool (version 0.91.5)

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

35

Version

0.91.5

License

GPL (>= 2)

Maintainer

Pierre-Alexandre Hebert

Last Published

August 29th, 2022

Functions in RclusTool (0.91.5)

KmeansQuick

Quick kmeans clustering
KmeansAutoElbow

Kmeans clustering with automatic estimation of number of clusters
RclusToolGUI

Username and user type selection
MainWindow

Main window
applyPreprocessing

Preprocessing application
abdPlot

Abundances barplot
bipartitionShi

Spectral clustering
ElbowPlot

Elbow Plot.
analyzePlot

Plot for data exploration/analysis
addOperation

Add operation
abdPlotTabs

Abundances barplots inside Tk tabs.
ElbowFinder

Elbow Finder
addIds2Sampling

Adding Ids To a Sampling
buildClusteringSample

Clustering loading
buildBatchTab

Batch process tab
clusterDensity

Clusters density computation
clusterSummary

Clusters summaries computation
buildConstraintsMatrix

Constraints matrices
buildNameOperation

Build Name Operation
buildImportTab

Build Import tab
computePcaNbDims

Number of dimensions for PCA
buildPreprocessTab

build Preprocess tab
buildSemisupTab

Semi-Supervised tab
addClustering

Clustering addition
abdPlotTabsGUI

Abundances barplots inside Tk tabs.
computeCKmeans

Constrained K-means clustering
computeSupervised

Supervised classification
computePcaSample

Principal Components Analysis
computeSpectralEmbeddingSample

Spectral embedding
computeEM

Expectation-Maximization clustering
detailOperation

detail Operation
.logoFrame

Logo frame in the graphical user interface
computeCSC

Constrained Spectral Clustering
computeGap

Gap computation
computeUnSupervised

Unsupervised clustering
computeGap2

Gap computation
computeGaussianSimilarity

Gaussian similarity
computeSampling

Sampling raw data matrix
computeKmeans

K-means clustering
computeItemsSampleGUI

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

Semi-supervised clustering
formatLabelSample

Labels formatting
formatParameterList

Format Parameter List
initSemisupTab

Semi-Supervised tab
initSupTab

supervised tab
convNamesPairsToIndexPairs

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

Conversion of element names to indexes
buildUnsupTab

Unsupervised tab
buildsupTab

Supervised tab
makeTitle

RclusTool makeTitle.
computeGaussianSimilarityZP

Gaussian similarity
computeItemsSample

Prediction of number of cells in colonies
extractProtos

Prototypes extraction
featSpaceNameConvert

Feature Space Name Conversion
cor.mtest

Correlation test.
matchNames

Match Names
initBatchTab

batch tab
guessFileEncoding

File Encoding Identification.
initImportTab

import tab
initUnsupTab

Unsupervised tab
loadSummary

Summaries loading
imgClassif

Images clustering
itemsModel

Predictive models computation for the number of cells in colonies
createResFolder

Results directories creation
makeFeatureSpaceOperations

Make operation config object to build feature spaces
critMNCut

Multiple Normalized Cut
messageConsole

RclusTool consoleMessage.
nameClusters

Clusters renaming
previewCSVfile

Preview CSV file
purgeSample

Sample purging
importLabelSample

Labels importation
plotProfileExtract

Profile and image plotting
saveManualProtos

Manual prototypes saving
plotSampleFeatures

2D-features scatter-plot
saveCounts

Count saving
countItems

Manually counting the number of cells in colonies
saveLogFile

Log file saving
plotDensity2D

plot Variables Density
saveCalcul

Object saving
saveClustering

Clustering saving
spectralClustering

Spectral clustering
importSample

Sample importation
sigClassif

Signals clustering
sortLabel

Clusters labels sorting
savePreprocess

Preprocessing exportation
plotProfile

Profile and image plotting
loadSample

Sample loading
loadPreviousRes

Previous clustering results loading
sortCharAsNum

Character vector numeric sorting
spectralClusteringNg

Spectral clustering
countItemsSampleGUI

GUI to manually count the number of cells in colonies
spectralEmbeddingNg

Spectral embedding
tkEmptyLine

RclusTool tkEmptyLine.
tk2draw.notetab

Draw in a Notetab.
readTrainSet

Training set reading
tk2notetab.RclusTool

RclusTool tk2notetab.
removeZeros

Zeros replacement
dropTrainSetVars

Parameters dropping
tkrplot.RclusTool

RclusTool tkrplot.
extractFeaturesFromSummary

Extraction of features from a summary object.
tkrreplot.RclusTool

RclusTool tkrreplot.
toStringDataFrame

To String Data Frame
initParameters

Parameters initialization
initPreprocessTab

build Preprocess tab
listDerivableFeatureSpaces

Builds list of derivable feature spaces
loadPreprocessFile

Preprocessing loading
measureMNCut

Multiple Normalized Cut
measureConstraintsOk

Rates of constraints satisfaction
saveSummary

Clusters summaries saving
search.neighbour

Search neighbour
tk2add.notetab

Add notetab.
tk2delete.notetab

Delete notetab inside a tk-notebook
updateClustersNames

Clusters names updating
visualizeSampleClustering

Interactive figure with 2D scatter-plot
FindNumberK

Automatic estimation of the number of clusters
KwaySSSC

Semi-supervised spectral clustering