[aut],
Joe Song [aut,
cre]Fast, optimal, and reproducible clustering algorithms for circular, periodic, or framed data. The algorithms introduced here are based on a core algorithm for optimal framed clustering the authors have developed (under review). The runtime of these algorithms is O(K N log^2 N), where K is the number of clusters and N is the number of circular data points. On a desktop computer using a single processor core, millions of data points can be grouped into a few clusters within seconds. One can apply the algorithms to characterize events along circular DNA molecules, circular RNA molecules, and circular genomes of bacteria, chloroplast, and mitochondria. One can also cluster climate data along any given longitude or latitude. Periodic data clustering can be formulated as circular clustering. The algorithms offer a general high-performance solution to circular, periodic, or framed data clustering.
| Version: | 0.0.3 |
| Imports: | Ckmeans.1d.dp, graphics, plotrix, Rcpp, stats |
| LinkingTo: | Rcpp |
| Suggests: | ape, bazar, ggplot2, knitr, reshape2, rmarkdown, testthat |
| Published: | 2020-12-18 |
| Author: | Tathagata Debnath
[aut],
Joe Song [aut,
cre] |
| Maintainer: | Joe Song <joemsong at cs.nmsu.edu> |
| License: | LGPL (≥ 3) |
| NeedsCompilation: | yes |
| Materials: | README NEWS |
| CRAN checks: | OptCirClust results |
| Reference manual: | OptCirClust.pdf |
| Vignettes: |
Circular genome clustering Performance of three circular data clustering algorithms Tutorial on optimal circular clustering Tutorial on optimal framed clustering |
| Package source: | OptCirClust_0.0.3.tar.gz |
| Windows binaries: | r-devel: OptCirClust_0.0.3.zip, r-release: OptCirClust_0.0.3.zip, r-oldrel: OptCirClust_0.0.3.zip |
| macOS binaries: | r-release: OptCirClust_0.0.3.tgz, r-oldrel: OptCirClust_0.0.3.tgz |
| Old sources: | OptCirClust archive |
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