K-means Clusterings
1. Pick k random points
2. Assign each point to closest of k points.
3. Compute centroid of each cluster to yield k new points.
4. Goto 2.
Guaranteed to give locally optimum answer.
Most commonly used.
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Slide 12 of 15 |
15 Oct 1998 |
lm@bitmover.com |