Ckmeans.1d.dp (3.4.6-3)

Optimal and Fast Univariate k-Means Clustering.

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.

Maintainer: Joe Song
Author(s): Joe Song [aut, cre], Haizhou Wang [aut]

License: LGPL (>= 3)

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

Released over 3 years ago.