K-Means is a clustering algorithm that partitions data into K distinct, non-overlapping subsets (clusters). The algorithm aims to minimize the within-cluster variance by iteratively assigning data points to the nearest cluster centroid and updating the centroids based on the mean of the assigned points. The process involves selecting K initial centroids, assigning each data point to the nearest centroid, recalculating the centroids based on the current cluster memberships, and repeating these steps until the centroids no longer change significantly. K-Means is widely used in customer segmentation, image compression, and pattern recognition. It is known for its simplicity and efficiency, but it requires specifying the number of clusters (K) in advance and can be sensitive to the initial placement of centroids, potentially leading to suboptimal solutions.