How does clustering suffer from the curse of initialization?

Poor initialization leads clustering algorithms like K-Means to converge to suboptimal solutions.

K-Means clustering is highly sensitive to initial centroid placement. Random initialization can lead to different final clusters due to local minima in the optimization space. Methods like K-Means++ improve initialization by spacing centroids probabilistically, reducing convergence to poor solutions.