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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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