Title: Optimal k-means clustering for one-dimensional data

Title: Optimal k-Means Clustering for One-Dimensional Data
Description: A dynamic programming algorithm for optimal one-dimensional k-means clustering. The algorithm minimizes the sum of squares of within-cluster distances. As an alternative to the standard heuristic k-means algorithm, this algorithm guarantees optimality and repeatability.

Description: A dynamic programming algorithm for optimal one-dimensional k-means clustering. The algorithm minimizes the sum of squares of within-cluster distances. As an alternative to heuristic k-means algorithms, this method guarantees optimality and reproducibility. Its advantage in efficiency and accuracy over k-means is increasingly pronounced as the number of clusters k increases.

Title: Optimal and Fast Univariate k-Means Clustering
Description: A fast dynamic programming algorithm for optimal univariate k-means clustering. The algorithm minimizes the sum of squares of within-cluster distances. As an alternative to heuristic k-means algorithms, this method guarantees optimality and reproducibility. Its advantage in efficiency and accuracy over heuristic k-means clustering is increasingly pronounced as the number of clusters k increases.

Authors@R: c(person("Joe", "Song", role = c("aut", "cre"),
person("Haizhou", "Wang", role = "aut"))
Author: Joe Song [aut, cre], Haizhou Wang [aut]
Description: A dynamic programming algorithm for optimal one-dimensional
k-means clustering. The algorithm minimizes the sum of squares of
within-cluster distances. As an alternative to heuristic k-means algorithms,
this method guarantees optimality and reproducibility. Its advantage in
efficiency and accuracy over k-means is increasingly pronounced as the

Description: A dynamic programming algorithm for optimal univariate k-means
clustering. Minimizing the sum of squares of within-cluster distances, the
algorithm guarantees optimality and reproducibility. Its advantage over
heuristic k-means algorithms in efficiency and accuracy is increasingly
pronounced as the number of clusters k increases. It provides an alternative
to heuristic k-means algorithms for univariate clustering.