K-means

K-means partitions a dataset into K clusters, where K is specified before training. The algorithm begins by selecting K initial cluster centers (centroids), assigns each data point to the nearest centroid, recalculates the centroids based on the assigned points, and repeats this process until the clusters stabilize or meet a stopping criterion.

K-means provides a simple and efficient way to discover patterns and group similar data without requiring labeled examples. It is computationally efficient for large datasets and serves as a foundational clustering technique. However, its performance depends on choosing an appropriate value of K and is best suited for clusters that are relatively compact and spherical.