K-means implementation is base on "Yingyang K-Means: A Drop-In Replacement of the Classic K-Means with Consistent Speedup". While it introduces some overhead and many conditional clauses which are bad for CUDA, it still shows 1.6-2x speedup against the Lloyd algorithm. K-nearest neighbors employ the same triangle inequality idea and require precalculated centroids and cluster assignments, similar to the flattened ball tree.
| Version: | 1.1.0 |
| Depends: | R (≥ 3.3.2) |
| Imports: | Rcpp (≥ 0.12.9) |
| LinkingTo: | Rcpp, RcppEigen |
| Suggests: | testthat |
| OS_type: | unix |
| Published: | 2019-03-22 |
| Author: | Vadim Markovtsev, Charles Determan |
| Maintainer: | Charles Determan <cdetermanjr at gmail.com> |
| License: | Apache License (≥ 2.0) | file LICENSE |
| NeedsCompilation: | yes |
| SystemRequirements: | CUDA 8.0 tookit, OpenMP 4.0 capable compiler |
| CRAN checks: | kmcudaR results |
| Reference manual: | kmcudaR.pdf |
| Package source: | kmcudaR_1.1.0.tar.gz |
| Windows binaries: | r-devel: not available, r-release: not available, r-oldrel: not available |
| macOS binaries: | r-release: not available, r-oldrel: not available |
| Old sources: | kmcudaR archive |
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