K-means clustering is a popular method used in data analysis to group similar data points together. It works by dividing a dataset into a specified number of clusters, represented by their centroids. The algorithm assigns each data point to the nearest centroid, then recalculates the centroids based on the assigned points, repeating this process until the clusters stabilize. This technique is widely used in various fields, including marketing, image processing, and pattern recognition. K-means is valued for its simplicity and efficiency, making it suitable for large datasets. However, it requires the user to define the number of clusters in advance.