Fast, optimal, and reproducible weighted univariate clustering by dynamic programming. Four types of problem including univariate k-means, k-median, k-segments, and multi-channel weighted k-means are solved with guaranteed optimality and reproducibility. The core algorithm minimizes the sum of (weighted) within-cluster distances using respective metrics. Its advantage over heuristic clustering in efficiency and accuracy is pronounced at a large number of clusters k. Weighted k-means can also process time series to perform peak calling. Multi-channel weighted k-means groups multiple univariate signals into k clusters. An auxiliary function generates histograms that are adaptive to patterns in data. This package provides a powerful set of tools for univariate data analysis with guaranteed optimality, efficiency, and reproducibility.
|Author||Joe Song [aut, cre] (<https://orcid.org/0000-0002-6883-6547>), Hua Zhong [aut] (<https://orcid.org/0000-0003-1962-2603>), Haizhou Wang [aut]|
|Maintainer||Joe Song <firstname.lastname@example.org>|
|License||LGPL (>= 3)|
|Package repository||View on CRAN|
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