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Given a set of data points as input Randomly assign each point to one of the k clusters

k -means Clustering Algorithm. Given a set of data points as input Randomly assign each point to one of the k clusters Repeat until convergence Calculate model of each of the k clusters Assign each point to the cluster with the closest model. k -means Clustering Example.

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Given a set of data points as input Randomly assign each point to one of the k clusters

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  1. k-means Clustering Algorithm • Given a set of data points as input • Randomly assign each point to one of the k clusters • Repeat until convergence • Calculate model of each of the k clusters • Assign each point to the cluster with the closest model

  2. k-means Clustering Example

  3. k-means Clustering Example Randomly assign each point to one of the clusters

  4. k-means Clustering Example Calculate center of each cluster

  5. k-means Clustering Example Calculate center of each cluster

  6. k-means Clustering Example Calculate center of each cluster

  7. k-means Clustering Example Assign each point to closest cluster center

  8. k-means Clustering Example Calculate center of each cluster

  9. k-means Clustering Example Calculate center of each cluster

  10. k-means Clustering Example Calculate center of each cluster

  11. k-means Clustering Example Assign each point to closest cluster center

  12. k-means Clustering Example Calculate center of each cluster

  13. k-means Clustering Example Calculate center of each cluster

  14. k-means Clustering Example Calculate center of each cluster

  15. k-means Clustering Example Convergence

  16. Does k-means always work?

  17. Does k-means always work?

  18. Does k-means always work?

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