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.

K-Means is a powerful unsupervised machine learning algorithm used to partition a dataset into a pre-determined number of distinct, non-overlapping clusters. It works by iteratively assigning data points to the nearest cluster "centroid" and then updating the centroid's position based on the mean of the assigned points. This guide breaks down the geometric intuition behind K-Means, explores its core assumptions and limitations, and introduces important variations you should k