The K-Means algorithm can produce suboptimal clusters if the initial centroids are poorly chosen. This blog explains the importance of centroid initialization, demonstrates the problem with examples, and introduces KMeans++—a smarter approach that ensures well-separated centroids for faster and more reliable clustering.