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RclusTool (version 0.91.3)

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

237

Version

0.91.3

License

GPL (>= 2)

Maintainer

Pierre-Alexandre Hebert

Last Published

February 4th, 2020

Functions in RclusTool (0.91.3)

addClustering

Clustering addition
addOperation

Add operation
extractProtos

Prototypes extraction
computeSemiSupervised

Semi-supervised clustering
computeSpectralEmbeddingSample

Spectral embedding
computeGap2

Gap computation
extractFeaturesFromSummary

Extraction of features from a summary object.
buildPreprocessTab

build Preprocess tab
buildNameOperation

Build Name Operation
computeGap

Gap computation
KwaySSSC

Semi-supervised spectral clustering
StringToTitle

String To Title.
buildConstraintsMatrix

Constraints matrices
buildsupTab

Supervised tab
applyPreprocessing

Preprocessing application
clusterSummary

Clusters summaries computation
initSupTab

supervised tab
analyzePlot

Plot for data exploration/analysis
computeCSC

Constrained Spectral Clustering
computePcaSample

Principal Components Analysis
computeCKmeans

Constrained K-means clustering
computeGaussianSimilarity

Gaussian similarity
KmeansAutoElbow

Kmeans clustering with automatic estimation of number of clusters
convNamesPairsToIndexPairs

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

Abundances barplot
computeKmeans

K-means clustering
buildImportTab

Build Import tab
computeSampling

Sampling raw data matrix
computeEM

Expectation-Maximization clustering
abdPlotTabs

Abundances barplots inside Tk tabs.
clusterDensity

Clusters density computation
convNamesToIndex

Conversion of element names to indexes
computeSupervised

Supervised classification
buildSemisupTab

Semi-Supervised tab
buildUnsupTab

Unsupervised tab
computeUnSupervised

Unsupervised clustering
formatParameterList

Format Parameter List
computePcaNbDims

Number of dimensions for PCA
featSpaceNameConvert

Feature Space Name Conversion
guessFileEncoding

File Encoding Identification.
initPreprocessTab

build Preprocess tab
computeGaussianSimilarityZP

Gaussian similarity
computeItems

Prediction of number of cells in colonies
importSample

Sample importation
formatLabelSample

Labels formatting
critMNCut

Multiple Normalized Cut
computeItemsGUI

GUI to estimate the number of cells in colonies for each cluster
cor.mtest

Correlation test.
itemsModel

Predictive models computation for the number of cells in colonies
countItemsGUI

GUI to manually count the number of cells in colonies
countItems

Manually counting the number of cells in colonies
matchNames

Match Names
detailOperation

detail Operation
initBatchTab

batch tab
measureConstraintsOk

Rates of constraints satisfaction
initUnsupTab

Unsupervised tab
createResFolder

Results directories creation
listDerivableFeatureSpaces

Builds list of derivable feature spaces
loadPreviousRes

Previous clustering results loading
sortLabel

Clusters labels sorting
loadSample

Sample loading
plotDensity2D

plot Variables Density
dropTrainSetVars

Parameters dropping
tk2add.notetab

Add notetab.
initImportTab

import tab
plotProfile

Profile and image plotting
initParameters

Parameters initialization
.logoFrame

Logo frame in the graphical user interface
sortCharAsNum

Character vector numeric sorting
spectralEmbeddingNg

Spectral embedding
imgClassif

Images clustering
removeZeros

Zeros replacement
saveClustering

Clustering saving
saveCounts

Count saving
saveCalcul

Object saving
loadPreprocessFile

Preprocessing loading
tk2delete.notetab

Delete notetab inside a tk-notebook
loadClusteringSample

Clustering loading
importLabelSample

Labels importation
nameClusters

Clusters renaming
tk2draw.notetab

Draw in a Notetab.
initSemisupTab

Semi-Supervised tab
measureMNCut

Multiple Normalized Cut
sigClassif

Signals clustering
plotProfileExtract

Profile and image plotting
loadSummary

Summaries loading
purgeSample

Sample purging
readTrainSet

Training set reading
search.neighbour

Search neighbour
spectralClustering

Spectral clustering
makeFeatureSpaceOperations

Make operation config object to build feature spaces
plotSampleFeatures

2D-features scatter-plot
savePreprocess

Preprocessing exportation
saveSummary

Clusters summaries saving
tk2notetab.RclusTool

RclusTool tk2notetab.
tkrplot.RclusTool

RclusTool tkrplot.
visualizeSampleClustering

Interactive figure with 2D scatter-plot
saveLogFile

Log file saving
updateClustersNames

Clusters names updating
spectralClusteringNg

Spectral clustering
saveManualProtos

Manual prototypes saving
toStringDataFrame

To String Data Frame
tkrreplot.RclusTool

RclusTool tkrreplot.
KmeansQuick

Quick kmeans clustering
RclusToolGUI

Username and user type selection
FindNumberK

Automatic estimation of the number of clusters
MainWindow

Main window
RclusTool

Graphical Toolbox for Clustering and Classification of Data Frames
ElbowFinder

Elbow Finder
bipartitionShi

Spectral clustering
buildBatchTab

Batch process tab
ElbowPlot

Elbow Plot.