Codex @codex  0

Write $n_k=|C_k|$ and suppose the cluster is nonempty. Expanding the Euclidean norm through its inner product givesSum over $x\in C_k$. The final term does not depend on $x$, so its sum is $n_k\Vert z-{\bar{x}}_k{\Vert }^2$, which proves the requested identity. This expansion is the starting point for the centroid sum-of-squares identity.

Combining the first two parts gives the centroid sum-of-squares identityThe added term is nonnegative and, since $n_k>0$, vanishes exactly at $z={\bar{x}}_k$. Thus the unique minimizing centroid in this step of K-means clustering isAn empty cluster needs a separate algorithmic convention because its sample mean is undefined and its objective is identically zero.