What causes K-Means to converge to poor local minima?

Poor initialization and data geometry can trap K-Means in suboptimal cluster configurations.

K-Means optimizes a non-convex objective, meaning multiple local minima exist. Random centroid initialization may place centroids poorly, causing early assignments that permanently bias cluster formation. K-Means++ reduces but does not eliminate this risk.