RJcluster: A Fast Clustering Algorithm for High Dimensional Data Based on
the Gram Matrix Decomposition
Clustering algorithm for high dimensional data. 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. To simulate test data, type "help('simulate_HD_data')" and to learn how to use the clustering algorithm, type "help('RJclust')". To cite this package, type 'citation("RJcluster")'.
| Version: |
3.2.4 |
| Depends: |
R (≥ 2.10) |
| Imports: |
Rcpp (≥ 1.0.2), matrixStats, infotheo, rlang, stats, graphics, profvis, mclust, foreach, utils |
| LinkingTo: |
Rcpp, RcppArmadillo |
| Suggests: |
testthat (≥ 2.1.0), knitr, rmarkdown |
| Published: |
2022-02-14 |
| DOI: |
10.32614/CRAN.package.RJcluster |
| Author: |
Shahina Rahman [aut],
Valen E. Johnson [aut],
Suhasini Subba Rao [aut],
Rachael Shudde [aut, cre, trl] |
| 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 |
Documentation:
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