Package: MajKMeans 0.1.0
MajKMeans: k-Means Algorithm with a Majorization-Minimization Method
A hybrid of the K-means algorithm and a Majorization-Minimization method to introduce a robust clustering. The reference paper is: Julien Mairal, (2015) <doi:10.1137/140957639>. The two most important functions in package 'MajKMeans' are cluster_km() and cluster_MajKm(). cluster_km() clusters data without Majorization-Minimization and cluster_MajKm() clusters data with Majorization-Minimization method. Both of these functions calculate the sum of squares (SS) of clustering.
Authors:
MajKMeans_0.1.0.tar.gz
MajKMeans_0.1.0.zip(r-4.5)MajKMeans_0.1.0.zip(r-4.4)MajKMeans_0.1.0.zip(r-4.3)
MajKMeans_0.1.0.tgz(r-4.5-any)MajKMeans_0.1.0.tgz(r-4.4-any)MajKMeans_0.1.0.tgz(r-4.3-any)
MajKMeans_0.1.0.tar.gz(r-4.5-noble)MajKMeans_0.1.0.tar.gz(r-4.4-noble)
MajKMeans_0.1.0.tgz(r-4.4-emscripten)MajKMeans_0.1.0.tgz(r-4.3-emscripten)
MajKMeans.pdf |MajKMeans.html✨
MajKMeans/json (API)
| # Install 'MajKMeans' in R: |
| install.packages('MajKMeans', repos = c('https://sheikhi-a.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/sheikhi-a/majkmeans/issues
Last updated 1 years agofrom:2e2cb82dea. Checks:1 OK, 8 NOTE. Indexed: yes.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Mar 07 2025 |
| R-4.5-win | NOTE | Mar 07 2025 |
| R-4.5-mac | NOTE | Mar 07 2025 |
| R-4.5-linux | NOTE | Mar 07 2025 |
| R-4.4-win | NOTE | Mar 07 2025 |
| R-4.4-mac | NOTE | Mar 07 2025 |
| R-4.4-linux | NOTE | Mar 07 2025 |
| R-4.3-win | NOTE | Mar 07 2025 |
| R-4.3-mac | NOTE | Mar 07 2025 |
Exports:clusters_kmclusters_MajKmEuclidkmeans
Dependencies:MASS
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| clustering results of the k-mean algorithm | clusters_km |
| clustering results of the majorized k-mean algorithm | clusters_MajKm |
| Euclidian distance | Euclid |
| k-means function | kmeans |
