This picture depicts an example of the K-Means Clustering process. Random data points are selected and serve as the initial clusters. How many there are depends on the value of k, which in this picture’s case, k = 2. It then measures the Euclidean distance between the 1st data point and the two initial clusters, and assigns it to whichever cluster is the closest. It repeats this process, calculates the mean values, and reclusters based on that. The process repeats until the cluster no longer changes (seen in final image).