k-means clustering is a popular machine learning algorithm used to group similar data points into clusters. It works by selecting a predefined number of clusters, denoted as k, and then assigning each data point to the nearest cluster center. The algorithm iteratively updates the cluster centers based on the mean of the assigned points until the positions stabilize. This method is widely used in various fields, such as image processing, market segmentation, and anomaly detection. Its simplicity and efficiency make it suitable for large datasets, although it requires the user to specify the number of clusters in advance.