Ckmeans.1d.dp (3.4.6-5)

Optimal and Fast Univariate k-Means Clustering.

A dynamic programming algorithm for optimal univariate k-means 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 analyze 1-D signals for segmentation and peak calling. An auxiliary function can generate adaptive histograms to make patterns in data stand out. For univariate data analysis, the package provides a powerful alternative to heuristic clustering algorithms.

Maintainer: Joe Song
Author(s): Joe Song [aut, cre], Haizhou Wang [aut]

License: LGPL (>= 3)

Uses: testthat, knitr, rmarkdown
Reverse suggests: DiffXTables, FunChisq, gsrc, vip, xgboost

Released about 3 years ago.