Fast optimal univariate clustering and segementation by dynamic programming. Three types of problem including univariate k-means, k-median, and k-segments are solved with guaranteed optimality and reproducibility. The core algorithm minimizes the sum of within-cluster distances using respective metrics. Its 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. In contrast to heuristic methods, this package provides a powerful set of tools for univariate data analysis with guaranteed optimality.
|Author||Joe Song [aut, cre], Haizhou Wang [aut]|
|Date of publication||2017-07-09 06:24:02 UTC|
|Maintainer||Joe Song <[email protected]>|
|License||LGPL (>= 3)|
|Package repository||View on CRAN|
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