Deducing bounds on the support of itemsets

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

Volumn 2682, Issue , 2004, Pages 214-233

  Calders, Toon ^a,b  

^a UNIVERSITY OF ANTWERP   (Belgium)

^b RESEARCH FOUNDATION FLANDERS FWO   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 35048835214     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-44497-8_11     Document Type: Article

References (20)

1. R. Agrawal et al. Mining association rules between sets of items in large databases. In Proc. ACM SIGMOD, pages 207-216, 1993.
2. R. Agrawal and R. Srikant. Fast algorithms for mining association rules. In Proc. VLDB, pages 487-499, 1994.
3. C.L. Blake and C.J. Merz. UCI Repository of machine learning databases [http:// www.ics.iici.edu/∼mlearn/MLRepository.html]. Irvine, CA: University of California, Dept. of Inf. and CS., 1998.
4. J.-F. Boulicaut and A. Bykowski. Frequent closures as a concise representation for binary data mining. In Proc. PaKDD, pages 62-73, 2000.
5. J.-F. Boulicaut et al. Approximation of frequency queries by means of free-sets. In Proc. PKDD, pages 75-85, 2000.
6. A. Bykowski and C. Rigotti. A condensed representation to find frequent patterns. In Proc. PODS, 2001.
7. A. Bykowski et al. Model-independent bounding of the supports of boolean formulae in binary data. In Proc. ECML-PKDD Workshop KDID, pages 20-31, 2002.
8. T. Calders and B. Goethals. Mining all non-derivable frequent itemsets. In Proc. PKDD, pages 74-85. Springer, 2002.
9. T. Calders and J. Paredaens. Axiomatization of frequent sets. In Proc. ICDT, pages 204-218, 2001.
10. R. Fagin et al. A logic for reasoning about probabilities. Information and Computation, 87(1,2):78-128, 1990.
11. D. Groth and E. Robertson. Discovering frequent itemsets in the presence of highly frequent items. In Proc. Workshop RBDM, in Conjunction with 14th Intl. Conf. On Applications of Prolog, 2001.
12. J. Han, J. Pei, and Y. Yin. Mining frequent patterns without candidate generation. In Proc. ACM SIGMOD, pages 1-12, 2000.
13. S. Hettich and S. D. Bay. The UCI KDD Archive. [http://kdd.ics.uci.edu]. Irvine, CA: University of California, Dept. of Inf. and CS., 1999.
14. M. Kryszkiewicz. Concise representation of frequent patterns based on disjunction-free generators. In Proc. ICDM, pages 305-312, 2001.
15. H. Mannila and H. Toivonen. Multiple uses of frequent sets and condensed representations. In Proc. KDD, 1996.
16. H. Mannila and H. Toivonen. Levelwise search and borders of theories in knowledge discovery. DMKD, 1(3):241-258, 1997.
17. N. Nilsson. Probabilistic logic. Artificial Intelligence, 28:71-87, 1986.
18. N. Pasquier et al. Discovering frequent closed itemsets for association rules. In Proc. ICDT, pages 398-416, 1999.
19. J. Pei et al. Closet: An efficient algorithm for mining frequent closed itemsets. In ACM SIGMOD Workshop DMKD, Dallas, TX, 2000.
20. M.J. Zaki and C. Hsiao. ChARM: An efficient algorithm for closed association rule mining. In Proc. ICDM, 2002.