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Mining Compressed Patterns: δ-clustering

  • Why compressed patterns?
    • too many, but less meaningful
  • Pattern distance measure  

\[ D(P_{1},P_{2})=1-\frac{|T(P_{1})\cap T(P_{2})|}{|T(P_{1})\cup T(P_{2})|}e \]

  • δ-clustering: For each pattern P, find all patterns which can be expressed by P and their distance to P are within δ (δ-cover)
  • All patterns in the cluster can be represented by P
  • Xin et al., “Mining Compressed Frequent-Pattern Sets”, VLDB’05
  • Closed frequent pattern
    • Report P1, P2, P3, P4, P5
    • Emphasize too much on support
    • no compression
  • Max-pattern, P3: info loss
  • A desirable output: P2, P3, P4



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