Unsupervised Learning: K-means
Last updated
Last updated
Non-data point that indicates the center of cluster as identified by K-means.
Deploy N centroids randomly (N proportional to the number of expected classes).
Assigns, randomly, data points to classes.
Repeat iteratively.
Compute the center of gravity of each class;
Centroid is repositioned in that center of gravity;
Update boundary.
Stop when updates become negligible.