Evolution and maintenance of frequent pattern space when transactions are removed

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4426 LNAI, Issue , 2007, Pages 489-497

  Feng, Mengling ^a   Dong, Guozhu ^b   Li, Jinyan ^c   Tan, Yap Peng ^a   Wong, Limsoon ^d  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^b WRIGHT STATE UNIVERSITY   (United States)

^c Institute for Infocomm Research

^d NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

Author keywords

[No Author keywords available]

Indexed keywords

DATA STRUCTURES; EQUIVALENCE CLASSES; PATTERN MATCHING;

EVOLUTION ANALYSIS; FREQUENT PATTERNS;

DATA MINING;

EID: 38049103381     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-71701-0_50     Document Type: Conference Paper

References (13)

1. R. Agrawal, et al. Mining association rules between sets of items in large databases. In SIGMOD, pages 207-216, 1993.
2. Y. Aumann, et al. Borders: An efficient algorithm for association generation in dynamic databases. In JIIS, (12) page 61-73, 1999.
3. R. J. Bayardo. Efficiently mining long patterns from databases. In SIGMOD, pages 85-93, 1998.
4. C. Chang, et al. Enhancing SWF for incremental association mining by itemset maintenance. In PAKDD, pages 301-312, 2003.
5. D. Cheung, et al. Maintenance of discovered association rules in large databases: An incremental update technique. In ICDE, pages 106-114, 1996.
6. D. Cheung, et al. A general incremental technique for maintaining discovered association rules. In Proc. 1996 DASFAA, pages 185-194, 1997.
7. K. Gouda, et al. GenMax: An efficient algorithm for mining maximal frequent itemsets. In Data Mining and Knowledge Discovery: An International Journal, 11: 1-20, 2005.
8. J. Han, et al. Mining frequent patterns without candidates generation. In SIGMOD, pages 1-12, 2000.
9. C. Lee, et al. Sliding window filtering: An efficient method for incremental mining on a time-variant database. Information Systems, 30(3):227-244, 2005.
10. H. Li, et al. Relative risk and odds ratio: A data mining perspective. In PODS, pages 368-377, 2005.
11. N. Pasquier, et al. Discovering frequent closed itemsets for association rules. In ICDT, pages 398-416, 1999.
12. A.A. Veloso, et al. Mining frequent itemsets in evolving databases. In SIAM, 2002.
13. S. Zhang, et al. A decremental algorithm for maintaining frequent itemsets in dynamic databases. In DaWak, pages 305-314, 2005.