In short, the algorithms are trying to accomplish different goals. K-nearest neighbor is a subset of supervised learning classification (or regression) algorithms (it takes a bunch of labeled points and uses them to learn how to label other points). It is supervised because you are trying to classify a point based on the known classification of other points. In contrast, K-means is a subset of unsupervised learning clustering algorithms (it takes a bunch of unlabeled…