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Constraint-Based Clustering Methods (I):Handling Hard Constraints

  • Handling hard constraints: Strictly respect the constraints in cluster assignments
  • Example: The COP-k-means algorithm
    • Generate super-instances for must-link constraints
      • Compute the transitive closure of the must-link constraints
      • To represent such a subset, replace all those objects in the subset by the mean.
      • The super-instance also carries a weight, which is the number of objects it represents
    • Conduct modified k-means clustering to respect cannot-link constraints
      • Modify the center-assignment process in k-means to a nearest feasible center assignment
      • An object is assigned to the nearest center so that the assignment respects all cannot-link constraints

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