These are completely different methods. The fact that they both have the letter K in their name is a coincidence.

K-means is a clustering algorithm that tries to partition a set of points into K sets (clusters) such that the points in each cluster tend to near each other. It is unsupervised because the points have no external classification.

K-nearest neighbors is a classification (or regression) algorithm that in order to determine the classification of a point, combines the classification of the K nearest points. It is supervised because you are trying to classify a point based on the known classification of other points.

These are completely different methods. The fact that they both have the letter K in their name is a coincidence.

K-means is a clustering algorithm that tries to partition a set of points into K sets (clusters) such that the points in each cluster tend to be near each other. It is unsupervised because the points have no external classification.

K-nearest neighbors is a classification (or regression) algorithm that in order to determine the classification of a point, combines the classification of the K nearest points. It is supervised because you are trying to classify a point based on the known classification of other points.

These are completely different methods. The fact that they both use the letter K is a coincidence.

K-means is a clustering algorithm that tries to partition a set of points into K sets. It is unsupervised because the points have no external classification.

K-nearest neighbors is a classification (or regression) algorithm that in order to determine the classification of a point, combines the classification of the K nearest points. It is supervised because you are trying to classify a point based on the known classification of other points.

These are completely different methods. The fact that they both have the letter K in their name is a coincidence.

K-means is a clustering algorithm that tries to partition a set of points into K sets (clusters) such that the points in each cluster tend to near each other. It is unsupervised because the points have no external classification.

K-nearest neighbors is a classification (or regression) algorithm that in order to determine the classification of a point, combines the classification of the K nearest points. It is supervised because you are trying to classify a point based on the known classification of other points.