1. Frequent Pattern Mining from Time-Fading Streams of Uncertain Data Carson Kai-Sang Leung and Fan Jiang DaWaK 2011 1

2. 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 2

3. 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. 3

4. Background  Mining from Static Database of Uncertain data  x:item  X:itemset  DB: transaction database  t i :transaction  the expected support of X in the DB can be computed by summing (over all transactionst 1,..., t |DB| ) the product (of existential probabilities of items within X): 4

5. Background 5

6. A Naive Algorithm ： TUF-Streaming(Naive) minsup=1.0 preMinsup=0.8 6

7. (Cont.) 7

8. 8

9. A Space-Saving Algorithm ： TUF- Streaming(Space) 9

10. (Cont.) 10

11. A Time-Saving Algorithm ： TUF- Streaming(Time) 11 Last batch Last batch’s expected support

12. (Cont.) 12

13. (Cont.) 13

14. Experimental Result 14

15. (Cont.) 15

16. 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. 16