Distributed approximate mining of frequent patterns

Proceedings of the ACM Symposium on Applied Computing

Volumn 1, Issue , 2005, Pages 529-536

  Silvestri, Claudio ^a   Orlando, Salvatore ^a  

^a CA' FOSCARI UNIVERSITY OF VENICE   (Italy)

Author keywords

Approximate data mining; Association mining; Distributed data mining; Frequent itemsets

Indexed keywords

ALGORITHMS; APPROXIMATION THEORY; COMPUTATIONAL METHODS; DATABASE SYSTEMS; PATTERN MATCHING;

APPROXIMATE DATA MINING; ASSOCIATION MINING; DISTRIBUTED DATA MINING; FREQUENT ITEMSETS;

DATA MINING;

EID: 33644541751     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1066677.1066796     Document Type: Conference Paper

References (30)

1. Workshop on frequent itemset mining implementations FIMI'03 in conjunction with ICDM'03. In fimi.cs.helsinki.fi, 2003.
2. R. Agarwal, C. Aggarwal, and V. V. V. Prasad. A tree projection algorithm for generation of frequent itemsets. Parallel and Distributed Computing, 2000.
3. R. Agrawal, T. Imielinski, and Swami A. Mining Associations between Sets of Items in Massive Databases. In Proc. of the ACM-SIGMOD 1993 Int'l Conf. on Management of Data, pages 207-216, 1993.
4. Rakesh Agrawal and Ramakrishnan Srikant. Fast algorithms for mining association rules. In Proc. 20th Int. Conf. Very Large Data Bases, VLDB, pages 487-499. Morgan Kaufmann, 1994.
5. A.Mueller. Fast sequential and parallel algorithms for association rules mining: A comparison. Technical Report CS-TR-3515, Univ. of Maryland, 1995.
6. R. J. Bayardo Jr. Efficiently Mining Long Patterns from Databases. In Proc. of the ACM SIGMOD Int. Conf. on Management of Data, pages 85-93, Seattle, Washington, USA, 1998.
7. Tom Brijs, Gilbert Swinnen, Koen Vanhoof, and Geert Wets. Using association rules for product assortment decisions: A case study. In Knowledge Discovery and Data Mining, pages 254-260, 1999.
8. Cheung, Han, Ng, Fu, and Fu. A fast distributed algorithm for mining association rules. In PDIS: International Conference on Parallel and Distributed Information Systems. IEEE Computer Society Technical Committee on Data Engineering, and ACM SIGMOD, 1996.
9. D.Burdick, M.Calimlim, and J.Gehrke. Mafia: a maximal frequent itemset for transactional databases. In Proc. of the International Conference on Data Endineering ICDE, 1999.
10. U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, editors. Advances in Knowledge Discovery and Data Mining. AAAI Press, 1998.
11. V. Ganti, J. Gehrke, and R. Ramakrishnan. Mining Very Large Databases. IEEE Computer, 32(8):38-45, 1999.
12. Karolien Geurts, Geert Wets, Tom Brijs, and Koen Vanhoof. Profiling high frequency accident locations using association rules. In Proceedings of the 82nd Annual Transportation Research Board, Washington DC. (USA), January 12-16, page 18pp, 2003.
13. E-H. S. Han, G.Karypis, and V.Kumar. Scalable parallel data mining for association rules. In IEEE Transaction on Knowledge and Data Engineering, 2000.
14. J. Han, J. Pei, and Y. Yin. Mining frequent patterns without candidate generation. In Proc. of the ACM SIGMOD Int. Conference on Management of Data, 2000.
15. B. Park and H. Kargupta. Distributed Data Mining: Algorithms, Systems, and Applications. In Data Mining Handbook, pages 341-358. IEA, 2002.
16. J.S. Park, M.S. Chen, and P.S. Yu. An Effective Hash Based Algorithm for Mining Association Rules. In Proceedings of 1995 ACM SIGMOD Int. Conf. on Management of Data, pages 175-186.
17. Srinivasan Parthasarathy. Efficient progressive sampling for association rules. In Proceedings of the 2002 IEEE International Conference on Data Mining (ICDM'02), page 354. IEEE Computer Society, 2002.
18. R.Agrawal and J.C.Shafer. Parallel mining of association rules. In IEEE Transaction On Knowledge and Data Engineering, 1996.
19. N. Ramakrishnan and A. Y. Grama. Data Mining: From Serendipity to Science. IEEE Computer, 32(8):34-37, 1999.
20. Ashoka Savasere, Edward Omiecinski, and Shamkant B. Navathe. An efficient algorithm for mining association rules in large databases. In VLDB'95, Proceedings of 21th International Conference on Very Large Data Bases, pages 432-444. Morgan Kaufmann, September 1995.
21. A. Schuster and R. Wolff. Communication Efficient Distributed Mining of Association Rules. In ACM SIGMOD, Santa Barbara, CA, April 2001.
22. A. Schuster, R. Wolff, and D. Trock. A High-Performance Distributed Algorithm for Mining Association Rules. In The Third IEEE International Conference on Data Mining (ICDM'03), Melbourne, FL, November 2003.
23. Shintani and Kitsuregawa. Hash based parallel algorithms for mining association rules. In PDIS: International Conference on Parallel and Distributed Information Systems. IEEE Computer Society Technical Committee on Data Engineering, and ACM SIGMOD, 1996.
24. S.Orlando, P.Palmerini, R.Perego, C.Lucchese, and F.Silvestri. kdci: a multi-strategy algorithm for mining frequent sets. In Proceedings of the workshop on Frequent Itemset Mining Implementations FIMI'03 in conjunction with ICDM'03, 2003.
25. S.Orlando, P.Palmerini, R.Perego, and F.Silvestri. Adaptive and resource-aware mining of frequent sets. In Proc. of the 2002 IEEE International Conference on Data Mining, ICDM, 2002.
26. S.Orlando, P.Palmerini, R.Perego, and F.Silvestri. An efficient parallel and distributed algorithm for counting frequent sets. In Proc. of Int. Conf. VECPAR 2002 - LNCS 2565, pages 197-204. Spinger, 2002.
27. V.C.Jensen and N.Soparkar. Frequent itemset counting across multiple tables. In In 4th PAcific Asia Conference on Knowledge Discovery and Data Minig, 2000.
28. R. Wolff and A. Schuster. Mining Association Rules in Peer-to-Peer Systems. In The Third IEEE International Conference on Data Mining (ICDM'03), Melbourne, FL, November 2003.
29. M. J. Zaki. Scalable algorithms for association mining. IEEE Transactions on Knowledge and Data Engineering, 12:372-390, May/June 2000.
30. Mohammed J. Zaki. Parallel and distributed association mining: A survey. In IEEE Concurrency, 1999.