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The K-Medoid Clustering Method

  • K-Medoids Clustering: Find representative objects (medoids) in clusters
    • PAM (Partitioning Around Medoids, Kaufmann & Rousseeuw 1987)
      • Starts from an initial set of medoids and iteratively replaces one of the medoids by one of the non-medoids if it improves the total distance of the resulting clustering
      • PAM works effectively for small data sets, but does not scale well for large data sets (due to the computational complexity)
  • Efficiency improvement on PAM
    • CLARA (Kaufmann & Rousseeuw, 1990): PAM on samples
    • CLARANS (Ng & Han, 1994): Randomized re-sampling

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