A fast dynamic programming algorithm for optimal univariate k-means clustering. The algorithm minimizes the sum of squares of within-cluster distances. As an alternative to heuristic k-means algorithms, this method guarantees optimality and reproducibility. Its advantage in efficiency and accuracy over heuristic k-means clustering is increasingly pronounced as the number of clusters k increases.
| Version: | 3.4.6 |
| Depends: | R (≥ 2.10.0) |
| Suggests: | testthat |
| Published: | 2016-06-02 |
| Author: | Joe Song and Haizhou Wang |
| Maintainer: | Joe Song <joemsong at cs.nmsu.edu> |
| BugReports: | NA |
| License: | LGPL (≥ 3) |
| URL: | NA |
| NeedsCompilation: | yes |
| Citation: | Ckmeans.1d.dp citation info |
| Materials: | NEWS |
| CRAN checks: | Ckmeans.1d.dp results |
| Reference manual: | Ckmeans.1d.dp.pdf |
| Package source: | Ckmeans.1d.dp_3.4.6.tar.gz |
| Windows binaries: | r-devel: Ckmeans.1d.dp_3.4.6.zip, r-release: Ckmeans.1d.dp_3.4.6.zip, r-oldrel: Ckmeans.1d.dp_3.4.6.zip |
| OS X Mavericks binaries: | r-release: Ckmeans.1d.dp_3.4.6.tgz, r-oldrel: Ckmeans.1d.dp_3.4.6.tgz |
| Old sources: | Ckmeans.1d.dp archive |
| Reverse suggests: | FunChisq, gsrc, xgboost |