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RclusTool (version 0.91)

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

License

GPL (>= 2)

Maintainer

Pierre-Alexandre Hebert

Last Published

November 6th, 2019

Functions in RclusTool (0.91)

KmeansQuick

Quick kmeans clustering
MainWindow

Main window
FindNumberK

Automatic estimation of the number of clusters
abdPlot

Abundances barplot
KmeansAutoElbow

Kmeans clustering with automatic estimation of number of clusters
ElbowFinder

Elbow Finder
RclusToolGUI

Username and user type selection
RclusTool

Graphical Toolbox for Clustering and Classification of Data Frames
KwaySSSC

Semi-supervised spectral clustering
ElbowPlot

Elbow Plot.
computeSupervised

Supervised classification
abdPlotTabs

Abundances barplots inside Tk tabs.
bipartitionShi

Spectral clustering
computeSpectralEmbeddingSample

Spectral embedding
buildImportTab

Build Import tab
addClustering

Clustering addition
computeItems

Prediction of number of cells in colonies
computeGaussianSimilarityZP

Gaussian similarity
computeCKmeans

Constrained K-means clustering
computeCSC

Constrained Spectral Clustering
guessFileEncoding

File Encoding Identification.
buildBatchTab

Batch process tab
featSpaceNameConvert

Feature Space Name Conversion
computePcaSample

Principal Components Analysis
importLabelSample

Labels importation
computePcaNbDims

Number of dimensions for PCA
extractProtos

Prototypes extraction
addOperation

Add operation
importSample

Sample importation
loadSample

Sample loading
imgClassif

Images clustering
analyzePlot

Plot for data exploration/analysis
loadSummary

Summaries loading
tk2draw.notetab

Draw in a Notetab.
computeEM

Expectation-Maximization clustering
matchNames

Match Names
initBatchTab

batch tab
nameClusters

Clusters renaming
makeFeatureSpaceOperations

Make operation config object to build feature spaces
buildConstraintsMatrix

Constraints matrices
buildPreprocessTab

build Preprocess tab
buildNameOperation

Build Name Operation
initImportTab

import tab
computeGap

Gap computation
plotDensity2D

plot Variables Density
clusterSummary

Clusters summaries computation
computeSampling

Sampling raw data matrix
clusterDensity

Clusters density computation
computeSemiSupervised

Semi-supervised clustering
createResFolder

Results directories creation
critMNCut

Multiple Normalized Cut
sigClassif

Signals clustering
formatLabelSample

Labels formatting
buildUnsupTab

Unsupervised tab
measureConstraintsOk

Rates of constraints satisfaction
initUnsupTab

Unsupervised tab
itemsModel

Predictive models computation for the number of cells in colonies
computeGap2

Gap computation
formatParameterList

Format Parameter List
computeGaussianSimilarity

Gaussian similarity
buildSemisupTab

Semi-Supervised tab
countItems

Manually counting the number of cells in colonies
measureMNCut

Multiple Normalized Cut
computeItemsGUI

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

GUI to manually count the number of cells in colonies
sortLabel

Clusters labels sorting
initSupTab

supervised tab
initSemisupTab

Semi-Supervised tab
plotProfileExtract

Profile and image plotting
plotProfile

Profile and image plotting
saveCounts

Count saving
removeZeros

Zeros replacement
saveLogFile

Log file saving
readTrainSet

Training set reading
tkrplot.RclusTool

RclusTool tkrplot.
tk2notetab.RclusTool

RclusTool tk2notetab.
tkrreplot.RclusTool

RclusTool tkrreplot.
computeKmeans

K-means clustering
sortCharAsNum

Character vector numeric sorting
convNamesToIndex

Conversion of element names to indexes
buildsupTab

Supervised tab
cor.mtest

Correlation test.
.logoFrame

Logo frame in the graphical user interface
detailOperation

detail Operation
initParameters

Parameters initialization
initPreprocessTab

build Preprocess tab
computeUnSupervised

Unsupervised clustering
dropTrainSetVars

Parameters dropping
spectralClustering

Spectral clustering
convNamesPairsToIndexPairs

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

Extraction of features from a summary object.
listDerivableFeatureSpaces

Builds list of derivable feature spaces
tk2add.notetab

Add notetab.
tk2delete.notetab

Delete notetab inside a tk-notebook
loadClusteringSample

Clustering loading
saveCalcul

Object saving
toStringDataFrame

To String Data Frame
visualizeSampleClustering

Interactive figure with 2D scatter-plot
saveClustering

Clustering saving
saveManualProtos

Manual prototypes saving
updateClustersNames

Clusters names updating
savePreprocess

Preprocessing exportation
loadPreprocessFile

Preprocessing loading
saveSummary

Clusters summaries saving
plotSampleFeatures

2D-features scatter-plot
loadPreviousRes

Previous clustering results loading
purgeSample

Sample purging
spectralEmbeddingNg

Spectral embedding
search.neighbour

Search neighbour
spectralClusteringNg

Spectral clustering
applyPreprocessing

Preprocessing application