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The K-Means Clustering Method
- Given k, the k-means algorithm is implemented in four steps:
- Partition objects into k nonempty subsets
- Compute seed points as the centroids of the clusters of the current partitioning (the centroid is the center, i.e., mean point, of the cluster)
- Assign each object to the cluster with the nearest seed point
- Go back to Step 2, stop when the assignment does not change
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