A type of algorithm used for clustering data into groups is the K-means algorithm. K-means clustering is an unsupervised learning technique that partitions data into K distinct clusters based on feature similarity. The algorithm iteratively assigns data points to the closest centroid and recalculates centroids until the assignments stabilize. It is widely used in data analysis to uncover hidden patterns, segment customer data, or simplify complex datasets. The simplicity and effectiveness of K-means make it a popular choice in various domains.