Hartigan's $k$-means algorithm has theoretical advantages over the standard Lloyd's variant, which has become synonymous with "$k$-means".
Yet, Hartigan's algorithm is far slower in practice due to its inherently sequential calculations.
We devise a Batched Hartigan's method that enables vectorized calculation by exploiting the triangle inequality to expose a set of parallel-safe updates and a smaller set of sequential updates.
This makes Hartigan's method competitive with Lloyd's in execution speed for the first time.