What is the theoretical justification behind K-Means++ initialization?

K-Means++ improves initialization by probabilistically spreading centroids based on squared distance.

It selects the first centroid randomly, then chooses subsequent centroids with probability proportional to squared distance from existing centroids. This reduces likelihood of poor clustering and provides O(log k) approximation guarantees.