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.
|Author||Joe Song [aut, cre], Haizhou Wang [aut]|
|Date of publication||2017-05-30 05:51:09 UTC|
|Maintainer||Joe Song <email@example.com>|
|License||LGPL (>= 3)|
|Package repository||View on CRAN|
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