Answer: (iv) All of the above

K-means requires all three inputs before it can run: (i) a distance metric (usually Euclidean) to decide which centroid a point is closest to and to compute within-cluster error; (ii) the number of clusters k, which must be supplied by the user because the algorithm has no way of discovering it; and (iii) an initial guess of the cluster centroids (seed points), which the algorithm then refines by alternating the assignment and update steps.

Since (i), (ii) and (iii) are each genuinely required, no single one of them is the answer — 'All of the above' is correct.