Answer: (i) Silhouette coefficient

The silhouette coefficient is the standard internal quality measure for clusters. For a point $i$, $s(i) = \dfrac{b(i) - a(i)}{\max\{a(i),\,b(i)\}}$, where $a(i)$ is the average distance to points in its own cluster and $b(i)$ is the average distance to points in the nearest neighbouring cluster. Values lie between $-1$ and $1$; a value near $1$ means the point is well matched to its own cluster and far from every other cluster. Averaging $s(i)$ over all points — or repeating this for several values of $k$ and choosing the best average — is a common way to assess and tune K-means.

Precision, accuracy and F1 score are label-based classification metrics: they need ground-truth classes to compare against. Clustering is unsupervised and has no ground-truth labels, so (ii), (iii) and (iv) do not apply.