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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