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CLARANS (“Randomized” CLARA) (1994)

  • CLARANS (A Clustering Algorithm based on Randomized Search) (Ng and Han’94)
    • Draws sample of neighbors dynamically
    • The clustering process can be presented as searching a graph where every node is a potential solution, that is, a set of k medoids
    • If the local optimum is found, it starts with new randomly selected node in search for a new local optimum
  • Advantages: More efficient and scalable than both PAM and CLARA
  • Further improvement: Focusing techniques and spatial access structures (Ester et al.’95)

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