Mining frequent itemsets using patricia tries

CEUR Workshop Proceedings

Volumn 90, Issue , 2003, Pages

  Pietracaprina, Andrea ^a   Zandolin, Dario ^a  

^a UNIVERSITY OF PADOVA   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL DATASETS; DEPTH-FIRST ALGORITHMS; ITEM SETS; MAIN MEMORY; MINING FREQUENT ITEMSETS; NOCV2; PATRICIA TRIE; SPACE EFFICIENT; SUPPORT THRESHOLD;

OPTIMIZATION;

EID: 84994526826     PISSN: 16130073     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (16)

1. R. Agrawal, C. Aggarwal, and V. Prasad. A tree projection algorithm for generation of frequent itemsets. Journal of Parallel and Distributed Computing, 61(3):350-371, 2001.
2. R. Agrawal, T. Imielinski, and A. Swami. Mining association rules between sets of items in large databases. In Proc. of the ACM SIGMOD Intl. Conference on Management of Data, pages 207-216, 1993.
3. R. Agrawal and R. Srikant. Fast algorithms for mining association rules. In Proc. of the 20th Very Large Data Base Conference, pages 487-499, 1994.
4. R. Bayardo. Efficiently mining long patterns from databases. In Proc. ot the ACM SIGMOD Intl. Conference on Management of Data, pages 85-93, 1998.
5. L. DeRose. Hardware Performance Monitor (HPM) toolkit. version 2.3.1. Technical report, Advanced Computer Technology Center, Nov. 2001.
6. A. Hafez, J. Deogun, and V. Raghavan. The item-set tree: A data structure for data mining. In Proc. of the 1st Int. Conference on Data Warehousing and Knowledge Discovery, LNCS 1676, pages 183-192, 1999.
7. J. Han, J. Pei, and Y. Yin. Mining frequent patterns without candidate generation. In Proc. of ACM SIGMOD Intl. Conference on Management of Data, pages 1-12, 2000.
8. D. Knuth. The Art of Computer Programming, volume 3: Sorting and Searching. Addison Wesley, Reading, MA, 1973.
9. J. Liu, Y. Pan, K. Wang, and J. Han. Mining frequent item sets by opportunistic projection. In Proc. of the 8th ACM SIGKDD Intl. Conference on Knowledge Discovery and Data Mining, pages 229-238, July 2002.
10. S. Orlando, P. Palmerini, R. Perego, and F. Silvestri. Adaptive resource-aware mining of frequent sets. In Proc. of the IEEE Intl. Conference on Data Mining, pages 338-345, Dec. 2002.
11. N. Pasquier, Y. Bastide, R. Taouil, and L. Lakhal. Discovering frequent closed itemsets for association rules. In Proc. of the 7th Int. Conference on Database Theory, pages 398-416, Jan. 1999.
12. J. Pei, J. Han, H. Lu, S. Nishio, S. Tang, and D. Yang. H-mine: Hyper-structure mining of frequent patterns in large databases. In Proc. of IEEE Intl. Conference on Data Mining, pages 441-448, 2001.
13. A. Savasere, E. Omiecinski, and S. Navathe. An efficient algorithm for mining association rules in large databases. In Proc. of the 21st Very Large Data Base Conference, pages 432-444, Sept. 1995.
14. K. Wang, L. Tang, J. Han, and J. Liu. Top down FP-Growth for association rule mining. In Proc. of the 6th Pacific-Asia Conf. on Advances in Knowledge Discovery and Data Mining, LNCS 2336, pages 334-340, May 2002.
15. M. Zaki. Scalable algorithms for association mining. IEEE Trans. on Knowledge and Data Engineering, 12(3):372-390, May-June 2000.
16. M. Zaki and K. Gouda. Fast vertical mining using diffsets. In Proc. of the 9th ACM SIGKDD Intl. Conference on Knowledge Discovery and Data Mining, Aug. 2003.