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An Efficient Candidate Pruning Technique for High Utility Pattern Mining

An Efficient Candidate Pruning Technique for High Utility Pattern Mining. Chowdhury Farhan Ahmed, Syed Khairuzzaman Tanbeer, Byeong-Soo Jeong, Young-Koo Lee PAKDD 2009. Outline. Motivation Definition Method Experimental Result Conclusion. Motivation.

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An Efficient Candidate Pruning Technique for High Utility Pattern Mining

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  1. An Efficient Candidate Pruning Technique for High Utility Pattern Mining Chowdhury Farhan Ahmed, Syed Khairuzzaman Tanbeer, Byeong-Soo Jeong, Young-Koo Lee PAKDD 2009

  2. Outline Motivation Definition Method Experimental Result Conclusion

  3. Motivation In this paper, we propose a novel tree-based candidate pruning technique HUC-Prune (high utility candidates prune) to efficiently mine high utility patterns.

  4. Definition I：I = {i1, i2, ......,im} be a set of items D：transaction database T：transaction，T={T1,T2,…..,Tn}， Ti ∈ D is a subset of I.

  5. Definition

  6. Definition

  7. Definition

  8. Definition

  9. Definition

  10. Definition

  11. HUC-prune minutil=106.75 twu(a)=44+37+74+102=257 twu(b)=347 twu(c)=89<minutil twu(d)=308 twu(e)=204

  12. HUC-prune

  13. HUC-prune

  14. HUC-prune

  15. HUC-prune 38+102

  16. HUC-prune All candidate：{b,e:140},{d,e:166},{e:204},{a,b:257}, {a,d:176},{a,b,d:176},{a:257},{b,d:228},{d:308},{b:347} High utility 6 itemsets:{a,b:134},{a,b,d:146},{b:132}, {b,d:154},{d:128},{d,e:134}

  17. Experimental Result

  18. Experimental Result

  19. Conclusion Extensive performance analyses show that our technique is very efficient in highutility pattern mining and it outperforms the existing algorithms in both denseand sparse datasets.

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