Ckmeans.1d.dp: Optimal, Fast, and Reproducible Univariate Clustering

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.

Package details

AuthorJoe Song [aut, cre] (<https://orcid.org/0000-0002-6883-6547>), Hua Zhong [aut] (<https://orcid.org/0000-0003-1962-2603>), Haizhou Wang [aut]
MaintainerJoe Song <joemsong@cs.nmsu.edu>
LicenseLGPL (>= 3)
Version4.3.2
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("Ckmeans.1d.dp")

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Ckmeans.1d.dp documentation built on March 26, 2020, 6:27 p.m.