These are oftentimes confused because they both have "K" in them, but they are quite different.
K Nearest Neighbor is a supervised algorithm. You feed it labeled data points first, and then based on its "nearest neighbor" and the k-value, it essentially labels those unlabeled data points.
K Means Clustering on the other hand is an unsupervised algorithm. You can feed it unlabeled data, and based on either a given k value or through it testing an assortment of k values on its own, creates its own clusters. By varying the number of clusters and finding the mean distance between those clusters, the algorithm can find the most mathematically efficient way to group things together in ways you may not have thought of.
If you would like some examples or have any questions, let me know in the comments.
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