Package: T4cluster 0.1.2

Kisung You

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.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://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

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

Pkgdown site:https://kisungyou.com

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

openblascppopenmp

2.95 score 3 stars 2 packages 9 scripts 167 downloads 35 exports 105 dependencies

Last updated 3 years agofrom:8adb54d675. Checks:OK: 1 NOTE: 1. Indexed: no.

TargetResultDate
Doc / VignettesOKDec 27 2024
R-4.5-linux-x86_64NOTEDec 27 2024

Exports:compare.adjrandcompare.randdpmeansEKSSfunhclustfunkmeans03Agen3SgenDONUTSgenLPgenSMILEYgmmgmm03Fgmm11Rgmm16Ggskmeanskmeanskmeans18BkmeansppLRRLRSCLSRMSMpcmpsmsc05Zsc09Gsc10Zsc11Ysc12LscNJWscSMscULspkmeansSSCSSQP

Dependencies:ADMMashbase64encbitbit64bitopsbslibcachemcliclustercodetoolscolorspaceCVXRdbscandeSolvedigestdoParallelECOSolveRevaluatefansifarverfastclusterfastmapfdafdsFNNfontawesomeforeachfsgenericsggplot2gluegmpgtablehdrcdehighrhtmltoolshtmlwidgetsisobanditeratorsjquerylibjsonlitekernlabKernSmoothknitrkslabdsvlabelinglatticelifecyclelocfitlpSolvemagrittrmaotaiMASSMatrixmclustmclustcompmemoisemgcvmimeminpack.lmmulticoolmunsellmvtnormnlmeosqppcaPPpillarpkgconfigpracmaR6rainbowRANNrappdirsrbibutilsRColorBrewerRcppRcppArmadilloRcppDERcppDistRcppEigenRCurlRdimtoolsRdpackrglrlangrmarkdownRmpfrRSpectrarstiefelRtsnesassscalesscatterplot3dscsshapestibbletinytexutf8vctrsviridisLitewithrxfunyaml

Readme and manuals

Help Manual

Help pageTopics
(+) Adjusted Rand Indexcompare.adjrand
(+) Rand Indexcompare.rand
DP-Means Clusteringdpmeans
Ensembles of K-SubspacesEKSS
Functional Hierarchical Clusteringfunhclust
Functional K-Means Clustering by Abraham et al. (2003)funkmeans03A
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
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