mining negative rules using grd
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2006/10/25 1
Mining negative rules using GRD
D. R. Thiruvady and G. I. Webb
PAKDD 2004
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Outline
• Introduction
• OPUS
• GRD
• Conclusion
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Introduction
• Association rule – A ==> B (A is antecedent ,B is consequent)
• Negative Rules– Either antecedent or consequent or both are negated
A B A B
A B
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Optimized Pruning for Unordered Search (OPUS)
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Generate Rule Discovery (GRD)
• Extends OPUS by remove the requirement that consequent be single variable
• K number of rules replace minimum support
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GRD
• Four measures with respect to a rule XY
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GRD
• Symbol
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Properties
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Properties
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Properties
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Properties
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GRD
• Symbol– CurrentLHS: Init ψ– AvailableLHS: Init Antecedent– AvailableRHS: Init Consequent
• Function– Insolution(ac): rule ac in solution– Proven(X): pruning rules provided to the alg
orithm prove the proposition X
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GRD algorithmPrune 1
Prune 2
Prune 3,4,5
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GRD algorithm2Prune 6
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Pruning
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Pruning
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Pruning
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Update Constraints
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Negative with GRD
support(A & B) support(A B) support(A & B)x x
support(A B) support(A) support(A B)
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Conclusion
• Disadvantage– Search space too large
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