What is K-Means clustering?

K-Means is an unsupervised clustering algorithm that partitions data into K clusters. The algorithm: 1) Randomly initialize K centroids. 2) Assign each point to the nearest centroid. 3) Update each centroid to the mean of its assigned points. 4) Repeat until convergence. The objective is to minimize within-cluster variance. Limitations: requires choosing K in advance, sensitive to initial centroids and outliers, assumes spherical clusters. The Elbow Method and Silhouette Score help choose optimal K.