This comprises new variations on k-means clustering algorithms for data analysis, based on novel seeding method for starting configurations of Lloyd-type methods. Variants of these heuristics lead to provably near-optimal clustering solutions applied to well-clusterable instances, and are candidates for faster-than-practice existing algorithms.