Implement k-means clustering on a shared doc.

Initialize centroids, assign each point to its nearest centroid, recompute means, and repeat until assignments or objective stabilize. Handle empty clusters and use vectorized squared distances; multiple k-means++ restarts reduce bad local optima.

Test empty input, one-element input, duplicates, boundary indices, invalid states, and the largest allowed size; state time and space complexity.