This package implements a dynamic programming algorithm to cluster one-dimensional data optimally, by minimizing the sum of squares of within-cluster distances. As an alternative to the heuristic k-means algorithm, the algorithm guarantees optimality and repeatability of clustering.
| Version: | 2.5 |
| Depends: | R (≥ 2.10.0) |
| Published: | 2012-10-08 |
| Author: | Joe Song and Haizhou Wang |
| Maintainer: | Haizhou Wang <hwang at cs.nmsu.edu> |
| License: | LGPL (≥ 3) |
| NeedsCompilation: | yes |
| Citation: | Ckmeans.1d.dp citation info |
| CRAN checks: | Ckmeans.1d.dp results |
| Package source: | Ckmeans.1d.dp_2.5.tar.gz |
| MacOS X binary: | Ckmeans.1d.dp_2.5.tgz |
| Windows binary: | Ckmeans.1d.dp_2.5.zip |
| Reference manual: | Ckmeans.1d.dp.pdf |
| Old sources: | Ckmeans.1d.dp archive |
| Reverse imports: | opm |