Ckmeans.1d.dp (3.4.6-1)

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.

Maintainer: Joe Song
Author(s): Joe Song and Haizhou Wang

License: LGPL (>= 3)

Uses: testthat
Reverse suggests: DiffXTables, FunChisq, gsrc, vip, xgboost

Released about 3 years ago.