Fast, optimal, and reproducible weighted univariate clustering by dynamic programming. Four types of problem including univariate k-means, k-median, k-segments, and multi-channel weighted k-means are solved with guaranteed optimality and reproducibility. The core algorithm minimizes the sum of (weighted) within-cluster distances using respective metrics. Its advantage over heuristic clustering in efficiency and accuracy is pronounced at a large number of clusters k. Weighted k-means can also process time series to perform peak calling. Multi-channel weighted k-means groups multiple univariate signals into k clusters. An auxiliary function generates histograms that are adaptive to patterns in data. This package provides a powerful set of tools for univariate data analysis with guaranteed optimality, efficiency, and reproducibility.
| Version: | 4.3.0 |
| Imports: | Rcpp, Rdpack |
| LinkingTo: | Rcpp |
| Suggests: | testthat, knitr, rmarkdown |
| Published: | 2019-09-07 |
| Author: | Joe Song [aut, cre], Hua Zhong [aut], Haizhou Wang [aut] |
| Maintainer: | Joe Song <joemsong at cs.nmsu.edu> |
| License: | LGPL (≥ 3) |
| NeedsCompilation: | yes |
| Citation: | Ckmeans.1d.dp citation info |
| Materials: | README NEWS |
| CRAN checks: | Ckmeans.1d.dp results |
| Reference manual: | Ckmeans.1d.dp.pdf |
| Vignettes: |
Tutorial: Optimal univariate clustering Note: Weight scaling in cluster analysis Tutorial: Adaptive versus regular histograms |
| Package source: | Ckmeans.1d.dp_4.3.0.tar.gz |
| Windows binaries: | r-devel: Ckmeans.1d.dp_4.3.0.zip, r-devel-gcc8: Ckmeans.1d.dp_4.3.0.zip, r-release: Ckmeans.1d.dp_4.3.0.zip, r-oldrel: Ckmeans.1d.dp_4.3.0.zip |
| OS X binaries: | r-release: Ckmeans.1d.dp_4.3.0.tgz, r-oldrel: Ckmeans.1d.dp_4.3.0.tgz |
| Old sources: | Ckmeans.1d.dp archive |
| Reverse imports: | emba, Tnseq |
| Reverse suggests: | DiffXTables, FunChisq, vip, xgboost |
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