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CLIQUE (Clustering In QUEst)

  • Agrawal, Gehrke, Gunopulos, Raghavan (SIGMOD’98)
  • Automatically identifying subspaces of a high dimensional data space that allow better clustering than original space
  • CLIQUE can be considered as both density-based and grid-based
    • It partitions each dimension into the same number of equal length interval
    • It partitions an m-dimensional data space into non-overlapping rectangular units
    • A unit is dense if the fraction of total data points contained in the unit exceeds the input model parameter
    • A cluster is a maximal set of connected dense units within a subspace

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