K-means clustering is a way of breaking a bunch of things into groups. Imagine you have a lot of toy cars. You could use K-means clustering to divide them into groups based on their size, color, or shape. To do this, you would first count the number of toy cars that you have. Let's say you counted 10 of them. Now use 10 points (called "centroids") in a grid to represent these toy cars. Move each centroid around until it is closest to a toy car. When they are all close enough, you have divided the toy cars into different groups. Each group is called a cluster.