Ckmeans.1d.dp (version 3.4.0-1)
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 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.