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.
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Optimal, Fast, and Reproducible Univariate Clustering
c(person("Joe", "Song", role = c("aut", "cre"),
email = "joemsong@cs.nmsu.edu"),
person("Hua", "Zhong", role = "aut"),
person("Haizhou", "Wang", role = "aut"))
Joe Song [aut, cre],
Hua Zhong [aut],
Haizhou Wang [aut]
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.

c(person("Joe", "Song", role = c("aut", "cre"),
comment = c(ORCID = "0000-0002-6883-6547"),
email = "joemsong@cs.nmsu.edu"),
person("Hua", "Zhong", role = "aut",
comment = c(ORCID = "0000-0003-1962-2603")),
person("Haizhou", "Wang", role = "aut"))