Mining approximate frequent itemsets from noisy data

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2005, Pages 721-724

  Liu, Jinze ^a   Paulsen, Susan ^a   Wang, Wei ^a   Nobel, Andrew ^a,b   Prins, Jan ^a  

^a UNIVERSITY OF NORTH CAROLINA   (United States)

^b University of North Carolina at Chapel Hill

Author keywords

[No Author keywords available]

Indexed keywords

DATA REDUCTION; DATABASE SYSTEMS; ERROR ANALYSIS; MATHEMATICAL MODELS; PATTERN MATCHING; SYSTEMATIC ERRORS;

APPROXIMATE FREQUENT ITEMSETS; FREQUENT ITEMSET MINING;

DATA ACQUISITION;

EID: 33746082673     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2005.93     Document Type: Conference Paper

References (11)

1. R. Agrawal, T. Imielinski, and A. Swami. Mining association rules between sets of items in large databases In SIGMOD 1993.
2. R. Agrawal, H. Mannila, R. Srikant, H. Toivonen, and A Verkamo. Fast discovery of association rules. In U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, editors, Advances in Knowledge Discove and Data Mining, chapter 12, pages 307328. AAAI Pre 1996.
3. C. Becquet , S. Blachon, B. Jeudy, J.F. Boulicaut, Gandrillon O. Strong-association-rule mining for large-scale gene-expression data analysis: a case study on humaMining gene expression databases for association rules, n SAGE data. Genome Biol. 2002
4. C. Creighton, S. Hanash .Mining gene expression databases for association rules. Bioinformatics. 2003 Jan;19(1):79-86.
5. J. Liu, S. Paulsen, W. Wang, A. Nobel, J. Prins. "Mining Approximate Frequent Itemset from Noisy Data". Technical Report(TR.05-015) of Department of Compuater Science, UNC-Chapel Hill, 2005 Jun.
6. H.C. Kum, S. Paulsen, W. Wang, Comparative Study of Sequential Pattern Mining Models, Studies in Computational Intelligence, Volume 6, Aug 2005, Pages 43-70.
7. J. Pei, A. K. Tung, and J. Han. Fault-tolerant frequent pattern mining: Problems and challenges. In Workshop on Research Issues in Data Mining and Knowledge Discovery, 2001.
8. J. K. Seppanen, H. Mannila. Dense Itemsets. In SIGKDD 2004.
9. M. Steinbach, P. N. Tan, V. Kumar. Support envelopes: a technique for exploring the structure of association patterns. In SIGKDD 2004.
10. P. J. Unmack. Biogeography of Australian freshwater ashes. Journal of Biogeography. Vol. 28: pages 1053-1089. Blackwell Science Ltd. 2001.
11. C. Yang, U. Fayyad, P. S. Bradley. Efficient discovery of error-tolerant frequent itemsets in high dimensions. In SIGKDD 2001.