Ckmeans.1d.dp (3.4.0-1)

Optimal k-Means Clustering for One-Dimensional Data.

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.

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

License: LGPL (>= 3)

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

Released over 3 years ago.