v2.16: Mixed-Precision K-Means

Many data scientists embark on their journey by implementing K-Means clustering, much like app developers starting with a calculator. But despite K-Means’ popularity, most implementations overlook the power of SIMD on modern CPUs. Efficient vector math, especially with single- and double-precision floating-point vectors, is challenging due to the computational cost of accuracy. Meanwhile, float16, bfloat16, and smaller types can fail under uneven distributions or when computing centroids for large clusters. So, what’s Unum’s solution? Mixed precision!

Thanks to strong community support and sponsorship from @sutoiku (LinkedIn, Website), we're introducing a high-performance K-Means implementation! It utilizes any numeric type for distance calculations, switching to float64 for centroid updates, a technique that boosts performance and enables billion-scale clustering on a single machine.