K-means clustering is a widely-used algorithm in machine learning that aims to partition data points into K clusters, each represented by a centroid. The process begins with the initialization step, where K centroids are randomly selected. Following this, each data point is assigned to the nearest centroid's cluster, a step known as assignment. The centroids are then updated to the mean position of the data points within their respective clusters. These steps are repeated until convergence, which occurs when the centroid positions no longer change or a predefined maximum number of iterations is reached. Typically, Euclidean distance is used to measure the proximity of data points to centroids, although other distance measures can also be employed. The number of clusters, K, must be specified in advance, and methods like the elbow method can help determine the appropriate value. K-means is easy to understand and implement, making it efficient for large datasets. However, its results can be sensitive to the initial selection of centroids and may not accurately identify non-spherical clusters. Additionally, it is sensitive to outliers. Common applications of K-means include customer segmentation, image compression, and anomaly detection.