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What Is the Problem with PAM?

  • Pam is more robust than k-means in the presence of noise and outliers because a medoid is less influenced by outliers or other extreme values than a mean
  • Pam works efficiently for small data sets but does not scale well for large data sets.
    • O(k(n-k)^2 ) for each iteration
      where n is # of data,k is # of clusters
  • Sampling-based method
    CLARA(Clustering LARge Applications)

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