Package: kkmeans 0.1.3
kkmeans: Fast Implementations of Kernel K-Means
Implementations several algorithms for kernel k-means. The default 'OTQT' algorithm is a fast alternative to standard implementations of kernel k-means, particularly in cases with many clusters. For a small number of clusters, the implemented 'MacQueen' method typically performs the fastest. For more details and performance evaluations, see Berlinski and Maitra (2025) <doi:10.1002/sam.70032>.
Authors:
kkmeans_0.1.3.tar.gz
kkmeans_0.1.3.zip(r-4.7-x86_64)kkmeans_0.1.3.zip(r-4.7-arm64)kkmeans_0.1.3.zip(r-4.6-x86_64)kkmeans_0.1.3.zip(r-4.6-arm64)kkmeans_0.1.3.zip(r-4.5-x86_64)
kkmeans_0.1.3.tgz(r-4.6-x86_64)kkmeans_0.1.3.tgz(r-4.6-arm64)kkmeans_0.1.3.tgz(r-4.5-x86_64)kkmeans_0.1.3.tgz(r-4.5-arm64)
kkmeans_0.1.3.tar.gz(r-4.7-arm64)kkmeans_0.1.3.tar.gz(r-4.7-x86_64)kkmeans_0.1.3.tar.gz(r-4.6-arm64)kkmeans_0.1.3.tar.gz(r-4.6-x86_64)
kkmeans_0.1.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
kkmeans/json (API)
| # Install 'kkmeans' in R: |
| install.packages('kkmeans', repos = c('https://jdberlinski.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/jdberlinski/kkmeans/issues
Last updated from:81d7508d3b. Checks:15 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 70 | ||
| linux-devel-x86_64 | OK | 100 | ||
| source / vignettes | OK | 204 | ||
| linux-release-arm64 | OK | 71 | ||
| linux-release-x86_64 | OK | 99 | ||
| macos-release-arm64 | OK | 92 | ||
| macos-release-x86_64 | OK | 187 | ||
| macos-oldrel-arm64 | OK | 88 | ||
| macos-oldrel-x86_64 | OK | 269 | ||
| windows-devel-arm64 | OK | 365 | ||
| windows-devel-x86_64 | OK | 100 | ||
| windows-release-arm64 | OK | 331 | ||
| windows-release-x86_64 | OK | 60 | ||
| windows-oldrel-x86_64 | OK | 86 | ||
| wasm-release | OK | 79 |
Exports:cluster_newget_kernel_matrixget_mknn_distjump_statkkmeansmatr
Dependencies:Rcpp
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Classify new data based on kkmeans result | cluster_new |
| Get the kernel matrix for a dataset | get_kernel_matrix |
| Get the average distance to each points k-nearest neighbor | get_mknn_dist |
| Function to get jump statistic for varying values of k | jump_stat |
| An Efficient Kernel K-Means Algorithm | kkmeans |
| Estimate the bandwidth parameter for a gaussian kernel using MATr | matr |
