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Bi-Clustering Methods

  • Real-world data is noisy: Try to find approximate bi-clusters
  • Methods: Optimization-based methods vs. enumeration methods
  • Optimization-based methods
    • Try to find a submatrix at a time that achieves the best significance as a bi-cluster
    • Due to the cost in computation, greedy search is employed to find local optimal bi-clusters
    • Ex. δ-Cluster Algorithm (Cheng and Church, ISMB’2000)
  • Enumeration methods
    • Use a tolerance threshold to specify the degree of noise allowed in the bi-clusters to be mined
    • Then try to enumerate all submatrices as bi-clusters that satisfy the requirements
    • Ex. δ-pCluster Algorithm (H. Wang et al.’ SIGMOD’2002, MaPle: Pei et al., ICDM’2003)

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