Fast optimal univariate clustering and segementation by dynamic programming. Three types of problem including univariate k-means, k-median, and k-segments are solved with guaranteed optimality and reproducibility. The core algorithm minimizes the sum of within-cluster distances using respective metrics. Its advantage over heuristic clustering algorithms in efficiency and accuracy is increasingly pronounced as the number of clusters k increases. Weighted k-means and unweighted k-segments algorithms can also optimally segment time series and perform peak calling. An auxiliary function generates histograms that are adaptive to patterns in data. In contrast to heuristic methods, this package provides a powerful set of tools for univariate data analysis with guaranteed optimality. Use four spaces when indenting paragraphs within the Description.
| Version: | 4.2.2 |
| Imports: | Rcpp (≥ 0.12.18) |
| LinkingTo: | Rcpp |
| Suggests: | testthat, knitr, rmarkdown |
| Published: | 2018-09-24 |
| 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: Linear weight scaling in cluster analysis Tutorial: Adaptive versus regular histograms |
| Package source: | Ckmeans.1d.dp_4.2.2.tar.gz |
| Windows binaries: | r-devel: Ckmeans.1d.dp_4.2.2.zip, r-release: Ckmeans.1d.dp_4.2.2.zip, r-oldrel: Ckmeans.1d.dp_4.2.2.zip |
| OS X binaries: | r-release: Ckmeans.1d.dp_4.2.2.tgz, r-oldrel: Ckmeans.1d.dp_4.2.2.tgz |
| Old sources: | Ckmeans.1d.dp archive |
| Reverse imports: | Tnseq |
| Reverse suggests: | FunChisq, vip, xgboost |
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