K-Means Clustering

The algorithm repeatedly assigns data points to the nearest cluster center and updates those centers until the grouping stabilizes. K-means is commonly used for segmentation, exploratory analysis, pattern discovery, and simplifying large datasets when labeled outcomes are not available. It is used when teams need models to learn patterns from data and generalize those patterns to new examples rather than relying only on hand-written rules.