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Subspace Clustering Method (I): Subspace Search Methods

  • Search various subspaces to find clusters
  • Bottom-up approaches
    • Start from low-D subspaces and search higher-D subspaces only when there may be clusters in such subspaces
    • Various pruning techniques to reduce the number of higher-D subspaces to be searched
    • Ex. CLIQUE (Agrawal et al. 1998)
  • Top-down approaches
    • Start from full space and search smaller subspaces recursively
    • Effective only if the locality assumption holds: restricts that the subspace of a cluster can be determined by the local neighborhood
    • Ex. PROCLUS (Aggarwal et al. 1999): a k-medoid-like method

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