A fast dynamic programming algorithm for optimal univariate clustering. Minimizing the sum of squares of within-cluster distances, the algorithm guarantees optimality and reproducibility. Its advantage over heuristic clustering algorithms in efficiency and accuracy is increasingly pronounced as the number of clusters k increases. With optional weights, the algorithm can also optimally segment time series and perform peak calling. An auxiliary function generates histograms that are adaptive to patterns in data. This package provides a powerful alternative to heuristic methods for univariate data analysis.
| Version: | 4.0.1 |
| Depends: | R (≥ 2.10.0) |
| Suggests: | testthat, knitr, rmarkdown |
| Published: | 2017-02-16 |
| Author: | Joe Song [aut, cre], Haizhou Wang [aut] |
| Maintainer: | Joe Song <joemsong at cs.nmsu.edu> |
| License: | LGPL (≥ 3) |
| NeedsCompilation: | yes |
| Citation: | Ckmeans.1d.dp citation info |
| Materials: | NEWS |
| CRAN checks: | Ckmeans.1d.dp results |
| Reference manual: | Ckmeans.1d.dp.pdf |
| Vignettes: |
Tutorial: Optimal univariate clustering Tutorial: Adaptive versus regular histograms |
| Package source: | Ckmeans.1d.dp_4.0.1.tar.gz |
| Windows binaries: | r-devel: Ckmeans.1d.dp_4.0.1.zip, r-release: Ckmeans.1d.dp_4.0.1.zip, r-oldrel: Ckmeans.1d.dp_4.0.1.zip |
| OS X Mavericks binaries: | r-release: Ckmeans.1d.dp_4.0.1.tgz, r-oldrel: Ckmeans.1d.dp_4.0.1.tgz |
| Old sources: | Ckmeans.1d.dp archive |
| Reverse imports: | gsrc, Tnseq |
| Reverse suggests: | FunChisq, xgboost |
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