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Frequent Pattern Mining fromTime-Fading Streams of Uncertain

DataCarson Kai-Sang Leung and Fan Jiang

DaWaK 2011

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Outline Motivation Background Method

A Naive Algorithm： TUF-Streaming(Naive) A Space-Saving Algorithm： TUF-

Streaming(Space) A Time-Saving Algorithm： TUF-Streaming(Time)

Experimental Result Conclusion

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Motivation In past few years, several mining algorithms

have been proposed to discover frequent patterns from uncertain data. However, most of them mine frequent patterns from static databases—but not dynamic streams—of uncertain data.

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Background Mining from Static Database of

Uncertain data x:item X:itemset DB: transaction database ti:transaction the expected support of X in the DB can be

computed by summing (over all transactionst1, ..., t|DB|) the product (of existential probabilities of items within X):

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Background Mining from Uncertain data Streams with

Sliding window Bi:batch X:itemset T:time DB: transaction database the expected support of X in the current sliding

window containing batches of uncertain data in Batches inclusive can be computed as follows:

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A Naive Algorithm： TUF-Streaming(Naive)

minsup=1.0preMinsup=0.8

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(Cont.)

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(Cont.)

minsup=1.0preMinsup=0.8

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A Space-Saving Algorithm： TUF-Streaming(Space)

minsup=1.0preMinsup=0.8

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(Cont.)

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A Time-Saving Algorithm： TUF-Streaming(Time) In have frequent: {a}=1.7,{b}=1.8,{b,c}=1.44,{b,c,d}=0.86,{b,d}=1.08,{c}=1.6, {c,d}=0.96,{d}=1.3Last

batch

Last batch’s expected support

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(Cont.) In have frequent:{a},{a,d},{b},{b,c},{c},

{d}

minsup=1.0preMinsup=

0.8

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(Cont.) In have frequent:{a},{b},{b,d},{c},{d}

minsup=1.0preMinsup=

0.8

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Experimental Result

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(Cont.)

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Conclusion In this paper, we proposed tree-based mining

algorithms that can be used for mining frequent patterns from dynamic streams of uncertain data with both time-fading and landmark models.