Transcript Mining Uncertain Data with Probabilistic Guarantees

MINING UNCERTAIN DATA WITH
PROBABILISTIC GUARANTEES
Liwen Sun,Reynold Cheng, DavidW.Cheung,
Jiefeng Cheng
SIGKDD 2010
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Outline
Motivation
 Problem Definition
 Method

P-Apriori Algorithm
 TODIS Algorithm
 Probabilistic Association Rules

Experimental Result
 Conclusion

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Motivation
The goals of this paper are:
 (1) propose a definition of frequent patterns and
association rules for the tuple uncertainty model.
 (2) develop efficient algorithms for mining
patterns and rules that are correct under PWS.

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The Possible World
P(W2)=T1.p*(1-T2.p)*(1-T3.p)*T4.p=0.6*0.5*0.3*1=0.09
P(W4)=(1-T1.p)*(1-T2.p)*T3.p*T4.p=0.4*0.5*0.7*1=0.
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Probabilistic Frequent Patterns(P-FP)
X:pattern
 sup(X):support count of pattern X
 sup({a})=1→0.29

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Probabilistic Frequent Patterns(P-FP)

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Probabilistic Association Rule(P-AR)

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Pruning Infrequent Patterns

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Dynamic-Programming Algorithm
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Divide-and-Conquer Algorithm
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Inverted Probability List

Lx:Inverted Probability List
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TODIS Algorithm

Phase 1: Generate candidate patterns.

Phase 2: Top-down support inheritance.
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Computing Association Rule Probability
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Computing Association Rule Probability
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Deriving Association Rules
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Experimental Result
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Conclusion

We studied efficient algorithms for extracting
frequent patterns from probabilistic databases.
The TODIS algorithm, when used together with
DC, yields the best performance.
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