K-means

K-means is a method used to group data points into clusters based on their similarities. Imagine organizing a collection of colorful balls into groups so that the ones in each group are more similar to each other than to those in other groups. The algorithm starts by choosing some initial centers (means) for these groups. It then assigns each data point to the closest center, recalculates the centers based on the assigned points, and repeats this process until the groups stabilize. This helps identify natural patterns or groupings within complex data efficiently.