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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.
The official source code to: Uncertainty Estimates of Predictions via a General Bias-Variance Decomposition (AISTATS'23)
SWIG bindings for best subset, best partition solvers, multiple flavors, multiple objectives
Approximate Bregman proximal gradient algorithm
Mirror Descent on Sphere Geometry