k-means is a popular clustering algorithm used in data analysis. It groups a set of data points into k distinct clusters based on their features. The algorithm starts by randomly selecting k initial centroids, which represent the center of each cluster. It then assigns each data point to the nearest centroid and updates the centroids based on the mean of the assigned points. The process repeats until the centroids no longer change significantly, indicating that the clusters are stable. k-means is widely used in various fields, including machine learning, image processing, and market segmentation, due to its simplicity and efficiency.