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Lloyd’s Algorithm is an algorithm to solve the K Means problem that is so ubiquitous it is often called the K Means algorithm, although we should distinguish the K Means problem from any particular algorithm used to solve it. Lloyd’s algorithm repeats the two alternating steps until convergence:

- Assign: For each datum \(x_n\), compute the distance between the datum and the \(K\) centroids. Assign the datum to the nearest centroid.
- Update centroids: For each cluster, set the centroid \(\mu_k = \frac{1}{|C_k|} \sum_{x_n \in C_k} x_n\) based on the previous assignments.

**Theorem 1**: Lloyd’s monotonically decreases the K Means objective until local convergence.

Proof: First, note that \(L(\{C_k\}) \geq 0\) because \(\lvert \lvert x_n - \mu_k \lvert \lvert^2 \geq 0\) and the sum of nonnegative terms is itself nonnegative. This means the objective function \(L(\{C_k\})\) cannot be lowered indefinitely.

We show that each step cannot increase the loss.

**Assign**: Note that

Per the rules of the assignment step, \(x_n\) is only reassigned if

\[\lvert \lvert x_n - \mu_{pre} \lvert \lvert^2 > \lvert \lvert x_n - \mu_{post} \lvert \lvert^2\]If \(\mu_{pre} = \mu_{post}\) i.e. the datum is not assigned to a new cluster, the loss is unchanged. If the datum is assigned to a new cluster, we know that

\[L_{post} - L_{pre} < 0\]-

\[\sum_n \lvert \lvert z_n - \bar{z} \lvert \lvert^2 \leq \sum_n \lvert \lvert z_n - z \lvert \lvert^2\]**Update centroids**: We first need to prove a lemma, that for a given set of data, the average of the data is the point with the smallest summed distanced to each datum. Specifically, let \(\bar{z} := \frac{1}{N}\sum_n z_n\) be the center and \(z\) be an arbitrary point. Then

Proof:

\[\begin{align*} &\sum_n \lvert \lvert z_n - z \lvert \lvert^2\\ &= \sum_n \lvert \lvert z_n - \bar{z} + \bar{z} - z \lvert \lvert^2\\ &= \sum_n \lvert \lvert z_n - \bar{z} \lvert \lvert^2 + \lvert \lvert \bar{z} - z \lvert \lvert^2 + 2 \sum_n (z_n \bar{z} - z_n z - \bar{z}\bar{z} + \bar{z} z)\\ &= \sum_n \lvert \lvert z_n - \bar{z} \lvert \lvert^2 + \lvert \lvert \bar{z} - z \lvert \lvert^2\\ &\geq \sum_n \lvert \lvert z_n - \bar{z} \lvert \lvert^2\\ \end{align*}\]This lemma tells us that relocated the centroids from the previously assigned points to the new points cannot increase the sum of squared distances. Hence, the loss is nonincreasing.