CRAN/E | OptCirClust

OptCirClust

Circular, Periodic, or Framed Data Clustering

Installation

About

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 (Debnath & Song 2021) doi:10.1109/TCBB.2021.3077573. 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.

Citation OptCirClust citation info

Key Metrics

Version 0.0.4
Published 2021-07-28 974 days ago
Needs compilation? yes
License LGPL (≥ 3)
CRAN checks OptCirClust results

Downloads

Yesterday 8 0%
Last 7 days 31 -24%
Last 30 days 163 -9%
Last 90 days 674 +27%
Last 365 days 2.381 -19%

Maintainer

Maintainer

Joe Song

joemsong@cs.nmsu.edu

Authors

Tathagata Debnath

aut

Joe Song

aut / cre

Material

README
NEWS
Reference manual
Package source

Vignettes

Circular genome clustering
Performance of three circular data clustering algorithms
Tutorial on optimal circular clustering
Tutorial on optimal framed clustering

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

OptCirClust archive

Imports

Ckmeans.1d.dp
graphics
plotrix
Rcpp
Rdpack
stats
reshape2

Suggests

ape
ggplot2
knitr
rmarkdown
testthat

LinkingTo

Rcpp

Reverse Imports

CircularSilhouette