Package: T4cluster 0.1.2

T4cluster: Tools for Cluster Analysis

Cluster analysis is one of the most fundamental problems in data science. We provide a variety of algorithms from clustering to the learning on the space of partitions. See Hennig, Meila, and Rocci (2016, ISBN:9781466551886) for general exposition to cluster analysis.

Authors:Kisung You [aut, cre]

T4cluster_0.1.2.tar.gz
T4cluster_0.1.2.zip(r-4.5)T4cluster_0.1.2.zip(r-4.4)T4cluster_0.1.2.zip(r-4.3)
T4cluster_0.1.2.tgz(r-4.4-x86_64)T4cluster_0.1.2.tgz(r-4.4-arm64)T4cluster_0.1.2.tgz(r-4.3-x86_64)T4cluster_0.1.2.tgz(r-4.3-arm64)
T4cluster_0.1.2.tar.gz(r-4.5-noble)T4cluster_0.1.2.tar.gz(r-4.4-noble)
T4cluster_0.1.2.tgz(r-4.4-emscripten)T4cluster_0.1.2.tgz(r-4.3-emscripten)
T4cluster.pdf |T4cluster.html
T4cluster/json (API)
NEWS

# Install 'T4cluster' in R:
install.packages('T4cluster', repos = c('https://kisungyou.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/kisungyou/t4cluster/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:

On CRAN:

4.26 score 6 stars 2 packages 8 scripts 221 downloads 44 exports 106 dependencies

Last updated 3 years agofrom:cf15db5292. Checks:OK: 1 WARNING: 8. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 02 2024
R-4.5-win-x86_64WARNINGNov 02 2024
R-4.5-linux-x86_64WARNINGNov 02 2024
R-4.4-win-x86_64WARNINGNov 02 2024
R-4.4-mac-x86_64WARNINGNov 02 2024
R-4.4-mac-aarch64WARNINGNov 02 2024
R-4.3-win-x86_64WARNINGNov 02 2024
R-4.3-mac-x86_64WARNINGNov 02 2024
R-4.3-mac-aarch64WARNINGNov 02 2024

Exports:ccphatecompare.adjrandcompare.randconsensus.aucconsensus.pacdpmeansEKSSephclustepmedoidsfunhclustfunkmeans03Agbphategen3SgenDONUTSgenLPgenSMILEYgmmgmm03Fgmm11Rgmm16Ggskmeanskmeanskmeans18BkmeansppLRRLRSCLSRMSMpcmpsmquality.CHquality.silsc05Zsc09Gsc10Zsc11Ysc12LscNJWscSMscULspkmeansSSCSSQPtestPHATE

Dependencies:ADMMashbase64encbitbit64bitopsbslibcachemclarabelcliclustercodetoolscolorspaceCVXRdbscandeSolvedigestdoParallelECOSolveRevaluatefansifarverfastclusterfastmapfdafdsFNNfontawesomeforeachfsgenericsggplot2gluegmpgtablehdrcdehighrhtmltoolshtmlwidgetsisobanditeratorsjquerylibjsonlitekernlabKernSmoothknitrkslabdsvlabelinglatticelifecyclelocfitlpSolvemagrittrmaotaiMASSMatrixmclustmclustcompmemoisemgcvmimeminpack.lmmulticoolmunsellmvtnormnlmeosqppcaPPpillarpkgconfigpracmaR6rainbowRANNrappdirsrbibutilsRColorBrewerRcppRcppArmadilloRcppDERcppDistRcppEigenRCurlRdimtoolsRdpackrglrlangrmarkdownRmpfrRSpectrarstiefelRtsnesassscalesscatterplot3dscsshapestibbletinytexutf8vctrsviridisLitewithrxfunyaml

Readme and manuals

Help Manual

Help pageTopics
Consensus Clustering with PHATE Geometryccphate
(+) Adjusted Rand Indexcompare.adjrand
(+) Rand Indexcompare.rand
Consensus Determination of K : Original Methodconsensus.auc
Consensus Determination of K : PAC methodconsensus.pac
DP-Means Clusteringdpmeans
Ensembles of K-SubspacesEKSS
Hierarchical Agglomerative Clustering for Empirical Distributionsephclust
K-Medoids Clustering for Empirical Distributionsepmedoids
Functional Hierarchical Clusteringfunhclust
Functional K-Means Clustering by Abraham et al. (2003)funkmeans03A
Generalized Bayesian Clustering with PHATE Geometrygbphate
Generate from Three 5-dimensional Subspaces in 200-dimensional space.gen3S
Generate Nested DonutsgenDONUTS
Generate Line and Plane Example with Fixed Number of ComponentsgenLP
Generate SMILEY DatagenSMILEY
Finite Gaussian Mixture Modelgmm
Ensemble of Gaussian Mixtures with Random Projectiongmm03F
Regularized GMM by Ruan et al. (2011)gmm11R
Weighted GMM by Gebru et al. (2016)gmm16G
Geodesic Spherical K-Meansgskmeans
Load 'household' datahousehold
K-Means Clusteringkmeans
K-Means Clustering with Lightweight Coresetkmeans18B
K-Means++ Clusteringkmeanspp
Low-Rank RepresentationLRR
Low-Rank Subspace ClusteringLRSC
Least Squares RegressionLSR
Bayesian Mixture of Subspaces of Different DimensionsMSM
Compute Pairwise Co-occurrence Matrixpcm
S3 method to predict class label of new data with 'MSM' objectpredict.MSM
Compute Posterior Similarity Matrixpsm
(+) CH indexquality.CH
(+) Silhouette Indexquality.sil
Spectral Clustering by Zelnik-Manor and Perona (2005)sc05Z
Spectral Clustering by Gu and Wang (2009)sc09G
Spectral Clustering by Zhang et al. (2010)sc10Z
Spectral Clustering by Yang et al. (2011)sc11Y
Spectral Clustering by Li and Guo (2012)sc12L
Spectral Clustering by Ng, Jordan, and Weiss (2002)scNJW
Spectral Clustering by Shi and Malik (2000)scSM
Spectral Clustering with Unnormalized LaplacianscUL
Spherical K-Means Clusteringspkmeans
Sparse Subspace ClusteringSSC
Subspace Segmentation via Quadratic ProgrammingSSQP
test using PHATEtestPHATE