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Ckmeans.1d.dp: Optimal and Fast Univariate k-Means Clustering

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.

Ckmeans.1d.dp.pdf