In this paper, we present FastKpp , a C++ implementation of an exact accelerated k -means + + algorithm using two geometry-aware filters: one based on the Triangle Inequality (TIE) and another based on point norms. Both provably preserve the original D 2 sampling distribution while avoiding redundant distance computations. The library provides a header-only API, a command-line tool, and a fully reproducible capsule. Experiments show substantial reductions in distance computations and speedups that increase with k (with typical median between 2 and 8). Furthermore, FastKpp outperforms other state-of-the-art exact acceleration techniques. Results are reproducible via the public GitHub repository and Code Ocean capsule. • Exact k-means++ acceleration with TIE and norm filters preserving D 2 sampling. • Large speedups that increase with k by reducing the number of distance evaluations. • The TIE version excels in low dimensions, while the TIE+Norm version leads in high-dimensional data with high norm variance. • Header-only C++ library with drop-in API and CLI. • Tight per-point lower bounds computed during seeding for later k-means accelerations.
Corominas et al. (Sun,) studied this question.