k-Means clustering is a popular and straightforward algorithm used to partition a dataset into distinct groups or clusters based on similarities among the data points. The goal of k-Means clustering is to divide the data into $k$ clusters, where each cluster is characterized by its centroid, which represents the center or average of the points within that cluster. The algorithm iteratively assigns each data point to the nearest centroid, then recalculates the centroids based on the mean position of the points assigned to each cluster.

This process continues until the assignments of data points to clusters no longer change significantly, indicating that the algorithm has converged to a stable solution. k-Means clustering is widely used due to its simplicity and efficiency, particularly in situations where the number of clusters is known beforehand. It is effective for identifying patterns and groupings in data, making it useful in various applications such as market segmentation, image compression, and data compression.