Spark library for generalized K-Means clustering. Supports general Bregman divergences. Suitable for clustering probabilistic data, time series data, high dimensional data, and very large data.

Fast and memory-efficient clustering + coreset construction, including fast distance kernels for Bregman and f-divergences.

The official source code to: Uncertainty Estimates of Predictions via a General Bias-Variance Decomposition (AISTATS'23)

Python implementation of Bregman Hard Clustering and Bregman Soft Clustering as a scikit-learn module.

Approximate Bregman proximal gradient algorithm

SWIG bindings for best subset, best partition solvers, multiple flavors, multiple objectives

Mirror Descent on Sphere Geometry