Clustering algorithm for high dimensional data. This algorithm is ideal for data where N << P. Assuming that P feature measurements on N objects are arranged in an N×P matrix X, this package provides clustering based on the left Gram matrix XX^T. When the P-dimensional feature vectors of objects are drawn independently from a K distinct mixture distribution, the N-dimensional rows of the modified Gram matrix XX^T/P converges almost surely to K distinct cluster means. This transformation/projection thus allows the clusters to be tighter with order of P. To simulate data, type "help('simulate_HD_data')" and to learn how to use the clustering algorithm, type "help('RJclust')".
| Version: | 2.5.0 |
| Depends: | R (≥ 2.10) |
| Imports: | Rcpp (≥ 1.0.2), matrixStats, infotheo, rlang, stats, graphics, profvis, mclust, foreach |
| LinkingTo: | Rcpp, RcppArmadillo |
| Suggests: | testthat (≥ 2.1.0), knitr, rmarkdown |
| Published: | 2021-04-06 |
| Author: | Rachael Shudde [aut, cre], Shahina Rahman [aut], Valen Johnson [aut] |
| Maintainer: | Rachael Shudde <rachael.shudde at gmail.com> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | yes |
| Materials: | README |
| CRAN checks: | RJcluster results |
| Reference manual: | RJcluster.pdf |
| Vignettes: |
RJclust_Vignette |
| Package source: | RJcluster_2.5.0.tar.gz |
| Windows binaries: | r-devel: RJcluster_2.5.0.zip, r-devel-UCRT: RJcluster_2.5.0.zip, r-release: RJcluster_2.5.0.zip, r-oldrel: RJcluster_2.5.0.zip |
| macOS binaries: | r-release (arm64): RJcluster_2.5.0.tgz, r-release (x86_64): RJcluster_2.5.0.tgz, r-oldrel: RJcluster_2.5.0.tgz |
| Old sources: | RJcluster archive |
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