K-Medoids is a variation of K-Means clustering algorithm


  • use a set of points from the original data set as anchors ("medoids")
  • then build clusters around them
  • each item is assigned to its closest representation from the data set
  • iterative approach


  • $J$: average similarity of each item to its centroid


  • require many iterations to converge
  • so it's slow: it's slow to compute $J$
  • doesn't always work well for sparse data
    • e.g. for text, not many docs have lots of terms in common
    • so similarities between such pairs are small and noisy
    • a single medoid may not contain all needed information to build a cluster around it


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