Ckmeans.1d.dp (3.3.3)

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 the standard heuristic k-means algorithm, this algorithm guarantees optimality and repeatability.

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.