Package: RclusTool 0.91.6

Pierre-Alexandre Hebert

RclusTool: Graphical Toolbox for Clustering and Classification of Data Frames

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) <doi:10.1016/j.patrec.2013.02.003> and the methodology provided by Wacquet et al. (2013) <doi:10.1007/978-3-642-35638-4_21>.

Authors:Guillaume Wacquet [aut], Pierre-Alexandre Hebert [aut, cre], Emilie Poisson [aut], Pierre Talon [aut]

RclusTool_0.91.6.tar.gz
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RclusTool.pdf |RclusTool.html✨
RclusTool/json (API)

# Install 'RclusTool' in R:
install.packages('RclusTool', repos = 'https://cloud.r-project.org')

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.00 score 258 downloads 115 exports 129 dependencies

Last updated 1 years agofrom:1163374f5c. Checks:3 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 24 2025
R-4.5-linuxOKMar 24 2025
R-4.4-linuxOKMar 24 2025

Exports:abdPlotabdPlotTabsabdPlotTabsGUIaddClusteringaddIds2SamplingaddOperationanalyzePlotapplyPreprocessingbipartitionShibuildBatchTabbuildClusteringSamplebuildConstraintsMatrixbuildImportTabbuildNameOperationbuildPreprocessTabbuildSemisupTabbuildsupTabbuildUnsupTabclusterDensityclusterSummarycomputeCKmeanscomputeCSCcomputeEMcomputeGapcomputeGap2computeGaussianSimilaritycomputeGaussianSimilarityZPcomputeItemsSamplecomputeItemsSampleGUIcomputeKmeanscomputePcaNbDimscomputePcaSamplecomputeSamplingcomputeSemiSupervisedcomputeSpectralEmbeddingSamplecomputeSupervisedcomputeUnSupervisedconvNamesPairsToIndexPairsconvNamesToIndexcor.mtestcountItemscountItemsSampleGUIcreateResFoldercritMNCutdetailOperationdropTrainSetVarsElbowFinderElbowPlotextractFeaturesFromSummaryextractProtosfeatSpaceNameConvertFindNumberKformatLabelSampleformatParameterListguessFileEncodingimgClassifimportLabelSampleimportSampleinitBatchTabinitImportTabinitParametersinitPreprocessTabinitSemisupTabinitSupTabinitUnsupTabitemsModelKmeansAutoElbowKmeansQuickKwaySSSClistDerivableFeatureSpacesloadPreprocessFileloadPreviousResloadSampleloadSummaryMainWindowmakeFeatureSpaceOperationsmakeTitlematchNamesmeasureConstraintsOkmeasureMNCutmessageConsolenameClustersplotDensity2DplotProfileplotProfileExtractplotSampleFeaturespreviewCSVfilepurgeSampleRclusToolGUIreadTrainSetremoveZerossaveCalculsaveClusteringsaveCountssaveLogFilesaveManualProtossavePreprocesssaveSummarysearch.neighboursigClassifsortCharAsNumsortLabelspectralClusteringspectralClusteringNgspectralEmbeddingNgtk2add.notetabtk2delete.notetabtk2draw.notetabtk2notetab.RclusTooltkEmptyLinetkrplot.RclusTooltkrreplot.RclusTooltoStringDataFrameupdateClustersNamesvisualizeSampleClustering

Dependencies:abindbackportsbase64encbootbroombslibcachemcarcarDataclasscliclustercolorspaceconclustcorrplotcowplotcpp11crosstalkdendextendDerivdigestdoBydplyrDTe1071ellipseemmeansestimabilityevaluatefactoextraFactoMineRfansifarverfastmapflashClustfontawesomeFormulafsgenericsggplot2ggpubrggrepelggsciggsignifgluegridExtragtablehighrhtmltoolshtmlwidgetshttpuvisobandjpegjquerylibjsonliteknitrlabelinglaterlatticelazyevalleapslifecyclelme4magrittrMASSMatrixMatrixModelsmclustmdamemoisemgcvmicrobenchmarkmimeminqammandmodelrmultcompViewmunsellmvtnormnlmenloptrnnetnumDerivpbkrtestpillarpkgconfigplyrpngpolynompromisesproxypurrrquantregR6randomForestrappdirsrbibutilsRColorBrewerRcppRcppEigenRdpackreformulasreshapereshape2rlangrmarkdownrstatixsassscalesscatterplot3dSearchTreesspSparseMstringistringrsurvivaltcltk2tibbletidyrtidyselecttinytextkrplotutf8vctrsviridisviridisLitewithrxfunyaml

Citation

This package relies on Constrained spectral embedding for K-way data clustering:

Wacquet G, Caillault EP, Hamad D, Hebert P (2013). “Constrained spectral embedding for K-way data clustering.” Pattern Recognition Letters, 34(9), 1009–1017. doi:10.1016/j.patrec.2013.02.003, https://hal.science/hal-01536663.

Wacquet G, Caillault EP, Hebert P (2013). “Semi-supervised K-Way Spectral Clustering with Determination of Number of Clusters.” In Madani, Kurosh, Dourado, Antonio, Rosa, Agostinho, Filipe, Joaquim (eds.), Computational Intelligence: Revised and Selected Papers of the International Joint Conference, IJCCI 2011, Paris, France, October 24-26, 2011, chapter Semi-supervised K-Way Spectral Clustering with Determination of Number of Clusters, 317–332. Springer Berlin Heidelberg, Berlin, Heidelberg. ISBN 978-3-642-35638-4, doi:10.1007/978-3-642-35638-4_21.

Corresponding BibTeX entries: 

  @Article{,
    title = {Constrained spectral embedding for K-way data clustering},
    author = {Guillaume Wacquet and Emilie Poisson Caillault and Denis
      Hamad and Pierre-Alexandre Hebert},
    journal = {Pattern Recognition Letters},
    year = {2013},
    volume = {34},
    number = {9},
    pages = {1009--1017},
    url = {https://hal.science/hal-01536663},
    pdf = {https://hal.science/hal-01536663/file/2013.PRL.pdf},
    doi = {10.1016/j.patrec.2013.02.003},
  }

  @InBook{,
    title = {Semi-supervised K-Way Spectral Clustering with
      Determination of Number of Clusters},
    author = {Guillaume Wacquet and Emilie Poisson Caillault and
      Pierre-Alexandre Hebert},
    editor = {{Madani} and {Kurosh} and {Dourado} and {Antonio} and
      {Rosa} and {Agostinho} and {Filipe} and {Joaquim}},
    booktitle = {Computational Intelligence: Revised and Selected
      Papers of the International Joint Conference, IJCCI 2011, Paris,
      France, October 24-26, 2011},
    chapter = {Semi-supervised K-Way Spectral Clustering with
      Determination of Number of Clusters},
    year = {2013},
    publisher = {Springer Berlin Heidelberg},
    address = {Berlin, Heidelberg},
    pages = {317--332},
    isbn = {978-3-642-35638-4},
    doi = {10.1007/978-3-642-35638-4_21},
  }