Ckmeans.1d.dp (4.2.0)

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Optimal and Fast Univariate Clustering.

A fast dynamic programming algorithmic framework to achieve optimal univariate k-means, k-median, and k-segments clustering. Minimizing the sum of respective within-cluster distances, the algorithms guarantee optimality and reproducibility. Their 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. This package provides a powerful alternative to heuristic methods for univariate data analysis.

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

License: LGPL (>= 3)

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

Released 25 days ago.

25 previous versions



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