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Mining Quantitative Associations

  • Techniques can be categorized by how numerical attributes, such as age or salary are treated
  • Static discretization based on predefined concept hierarchies (data cube methods)
  • Dynamic discretization based on data distribution (quantitative rules, e.g., Agrawal & Srikant@SIGMOD96)
  • Clustering: Distance-based association (e.g., Yang & Miller@SIGMOD97)
    • One dimensional clustering then association
  • Deviation: (such as Aumann and Lindell@KDD99)
      • Sex = female => Wage: mean=$7/hr (overall mean = $9)

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