Basic usage | Custom cluster ordering | Missing data | High dimensions | Comparison with R vanilla kmeans | Random seed
Package: tglkmeans 0.6.4
Aviezer Lifshitz
tglkmeans: Efficient Implementation of K-Means++ Algorithm
Efficient implementation of K-Means++ algorithm. For more information see (1) "kmeans++ the advantages of the k-means++ algorithm" by David Arthur and Sergei Vassilvitskii (2007), Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms, Society for Industrial and Applied Mathematics, Philadelphia, PA, USA, pp. 1027-1035, and (2) "The Effectiveness of Lloyd-Type Methods for the k-Means Problem" by Rafail Ostrovsky, Yuval Rabani, Leonard J. Schulman and Chaitanya Swamy <doi:10.1145/2395116.2395117>.
Authors:
tglkmeans_0.6.4.tar.gz
tglkmeans_0.6.4.tgz(r-4.6-x86_64)tglkmeans_0.6.4.tgz(r-4.6-arm64)tglkmeans_0.6.4.tgz(r-4.5-x86_64)tglkmeans_0.6.4.tgz(r-4.5-arm64)
tglkmeans_0.6.4.tar.gz(r-4.7-arm64)tglkmeans_0.6.4.tar.gz(r-4.7-x86_64)tglkmeans_0.6.4.tar.gz(r-4.6-arm64)tglkmeans_0.6.4.tar.gz(r-4.6-x86_64)
tglkmeans_0.6.4.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
tglkmeans/json (API)
| # Install 'tglkmeans' in R: |
| install.packages('tglkmeans', repos = c('https://tanaylab.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/tanaylab/tglkmeans/issues
Pkgdown/docs site:https://tanaylab.github.io
algorithms-implementedkmeanscpp
Last updated from:e3ba756032. Checks:13 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 171 | ||
| linux-devel-x86_64 | OK | 193 | ||
| source / vignettes | OK | 201 | ||
| linux-release-arm64 | OK | 170 | ||
| linux-release-x86_64 | OK | 172 | ||
| macos-release-arm64 | OK | 119 | ||
| macos-release-x86_64 | OK | 243 | ||
| macos-oldrel-arm64 | OK | 130 | ||
| macos-oldrel-x86_64 | OK | 533 | ||
| windows-devel | OK | 73 | ||
| windows-release | OK | 71 | ||
| windows-oldrel | OK | 68 | ||
| wasm-release | OK | 138 |
Exports:%>%downsample_matrixmatch_clusterspredict_tgl_kmeanssimulate_datatest_clusteringTGL_kmeansTGL_kmeans_tidytglkmeans.set_parallel
Dependencies:clidplyrgenericsgluelatticelifecyclemagrittrMatrixpillarpkgconfigpurrrR6RcppRcppParallelrlangtgstattibbletidyselectutf8vctrswithr
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Downsample the columns of a matrix to a target number | downsample_matrix |
| Match clusters to true clusters | match_clusters |
| Predict cluster assignments for new data | predict_tgl_kmeans |
| Simulate normal data for kmeans tests | simulate_data |
| Test clustering performance | test_clustering |
| kmeans++ with return value similar to R kmeans | TGL_kmeans |
| TGL kmeans with 'tidy' output | TGL_kmeans_tidy |
| Set parallel threads | tglkmeans.set_parallel |
