What is K-Means Clustering and how does it work?

K-Means is an unsupervised learning algorithm that partitions data into K clusters by minimizing distance between points and cluster centroids.

K-Means works by iteratively assigning data points to the nearest centroid and then recomputing centroids as the mean of assigned points. The objective is to minimize within-cluster sum of squared distances (WCSS). It converges when assignments stop changing or centroids stabilize.