Approximate frequent itemset mining in the presence of random noise

Soft Computing for Knowledge Discovery and Data Mining

Volumn , Issue , 2008, Pages 363-389

  Cheng, Hong ^a   Yu, Philip S ^b   Han, Jiawei ^a  

^a UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

^b IBM T J WATSON RESEARCH CENTER   (United States)

Author keywords

approximate frequent itemset; core pattern recovery; error tolerant itemset

Indexed keywords

EID: 65449157912     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1007/978-0-387-69935-6_15     Document Type: Chapter

References (31)

1. M. Abramowitz and I. A. Stegun. Handbook of Mathematical Functions with For-mulas, Graphs, and Mathematical Tables. Dover, 1964.
2. R. Agrawal, T. Imielinski, and A. Swami. Mining association rules between sets of items in large databases. in Proc. SIGMOD'93, pages 207-216, May 1993.
3. R. Agrawal and R. Srikant. Fast algorithms for mining association rules. in Proc. VLDB'94, pages 487-499, Sept. 1994.
4. R. Agrawal and R. Srikant. Privacy-preserving data mining. in Proc. of SIGMOD, pages 439-450, 2000.
5. R. J. Bayardo. Effciently mining long patterns from databases. in Proc. SIG-MOD'98, pages 85-93, June 1998.
6. J.F. Boulicaut, A. Bykowski, and C. Rigotti. Approximation of frequency queries by means of free-sets. in Principles of Data Mining and Knowledge Discovery, pages 75-85, 2000.
7. D. Burdick, M. Calimlim, and J. Gehrke. MAFIA: A maximal frequent itemset algorithm for transactional databases. in Proc. ICDE'01, pages 443-452, April 2001.
8. H. Cheng, X. Yan, J. Han, and C. Hsu. Discriminative frequent pattern analysis for effective classification. in Proc. 2007 Int. Conf. Data Engineering (ICDE'07), Istanbul, Turkey, April 2007.
9. H. Cheng, P. S. Yu, and J. Han AC-Close: Effciently Mining Approximate Closed Itemsets by Core Pattern Recovery. in Proc. of ICDM, pages 839-844, 2006.
10. G. Cong, K. Tan, A. Tung, and X. Xu. Mining top-k covering rule groups for gene expression data. in Proc. of SIGMOD, pages 670-681, 2005.
11. FIMI: Frequent itemset mining implementations repository. http://fimi.cs.helsinki.fi, 2003.
12. J. Han, J. Pei, and Y. Yin. Mining frequent patterns without candidate generation. in Proc. SIGMOD'00, pages 1-12, May 2000.
13. W. Li, J. Han, and J. Pei. CMAR: Accurate and effcient classification based on multiple class-association rules.In Proc. of ICDM, pages 369-376, 2001.
14. B. Liu, W. Hsu, and Y. Ma. Integrating classification and association rule mining. in Proc. of KDD, pages 80-86, 1998.
15. J. Liu, S. Paulsen, W. Wang, A. Nobel, and J. Prins. Mining approximate frequent itemset from noisy data. in Technical report, Department of Computer Science, TR05-015, 2005.
16. J. Liu, S. Paulsen, X. Sun, W. Wang, A. Nobel, and J. Prins. Mining approximate frequent itemsets in the presence of noise: Algorithm and analysis. in Proc. SDM'06, pages 405-416, April 2006.
17. H. Mannila and H. Toivonen. Multiple uses of frequent sets and condensed repre-sentations. in Knowledge Discovery and Data Mining, pages 189-194, 1996.
18. J. Pei, G. Dong, W. Zou, and J. Han. Mining condensed frequent pattern bases. in Knowledge and Information Systems, volume 6 of 5, pages 570-594, 2004.
19. J. Pei, J. Han, and R. Mao. CLOSET: An effcient algorithm for mining frequent closed itemsets. in Proc. DMKD'00, pages 11-20, May 2000.
20. W. H. Press, B. P. Flannery, S. A. Teukolsky, and W. T. Vetterling. Numerical Recipes in C. Cambridge, 2nd edition, 1992.
21. J. Seppäanen and H. Mannila. Dense itemsets. in Proc. of KDD, pages 683-688, 2004.
22. M. Steinbach, P. Tan, and V. Kumar. Support envelopes: A technique for exploring the structure of association patterns. in Proc. KDD'04, pages 296-305, Aug. 2004.
23. UCI: machine learning repository. http://www.ics.uci.edumlearn/MLSummary. html, 2007.
24. V. Verykios, E. Bertino, I. Fovino, L. Provenza, Y. Saygin, and Y. Theodoridis. State-of-the-art in privacy preserving data mining. SIGMOD Record, 3:50-57, 2004.
25. K. Wang, C. Xu, and B. Liu. Clustering transactions using large items. in Proc. of CIKM, pages 483-490, 1999.
26. X. Yan, M. R. Mehan, Y. Huang, M. S. Waterman, P. S. Yu, and X. J. Zhou. A graph-based approach to systematically reconstruct human transcriptional regulatory modules. in Proc. of ISMB, 2007.
27. X. Yan, P. S. Yu, and J. Han. Graph Indexing: A frequent structure-based ap-proach. in Proc. of SIGMOD, pages 335-346, 2004.
28. C. Yang, U. Fayyad, and P. S. Bradley. Effcient discovery of error-tolerant frequent itemsets in high dimensions. in Proc. KDD'01, pages 194-203, Aug. 2001.
29. M. J. Zaki. Scalable algorithms for association mining. IEEE Trans. Knowledge and Data Engineering, 12:372-390, 2000.
30. M. J. Zaki and C. J. Hsiao. CHARM: An effcient algorithm for closed itemset mining. in Proc. SDM'02, pages 457-473, April 2002.
31. F. Zhu, X. Yan, J. Han, P. S. Yu, and H. Cheng. Mining colossal frequent patterns by core pattern fusion. in Proc. 2007 Int. Conf. Data Engineering (ICDE'07), Istanbul, Turkey, April 2007.