Theoretical bounds on the size of condensed representations

Lecture Notes in Computer Science

Volumn 3377, Issue , 2005, Pages 46-65

  Dexters, Nele ^a   Calders, Toon ^a  

^a UNIVERSITY OF ANTWERP   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION THEORY; DATABASE SYSTEMS; SEMANTICS; SET THEORY; STATISTICAL METHODS;

CONDENSED REPRESENTATIONS; INDUCTIVE DATABASES; ITEMSET SPACE;

COMPUTER SCIENCE;

EID: 24144462197     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-540-31841-5_4     Document Type: Conference Paper

References (18)

1. R. Agrawal, T. Imilienski, and A. Swami. Mining association rules between sets of items in large databases. In Proc. ACM SIGMOD Int. Conf. Management of Data, pages 207-216, Washington, B.C., 1993.
2. R. Agrawal and R. Srikant. Privacy-preserving data mining. In Proc. ACM SIGMOD Int. Conf. Management of Data, pages 439-450, 2000.
3. C.L. Blake and C.J. Merz. The UCI Repository of machine learning databases [http://www.ics.uci.edu/~mlearn/MLRepository.html]. Irvine, CA: University of California, Department of Information and Computer Science, 1998.
4. J.-F. Boulicaut, A. Bykowski, and C. Rigotti. Approximation of frequency queries by means of free-sets. In Proc. PKDD Int. Conf. Principles of Data Mining and Knowledge Discovery, pages 75-85, 2000.
5. A. Bykowski and C. Rigotti. A condensed representation to find frequent patterns. In Proc. PODS Int. Conf. Principles of Database Systems, 2001.
6. T. Calders. Deducing bounds on the frequency of itemsets. In EDBT Workshop DTDM Database Techniques in Data Mining, 2002.
7. T. Calders. Axiomatization and Deduction Rules for the Frequency of Itemsets. PhD thesis, University of Antwerp, Belgium, 2003. http://win-www.ruca.ua.ac.be/u/calders/download/thesis.pdf.
8. T. Calders and B. Goethals. Minimal k-free representations of frequent sets. In Proc. PKDD Int. Conf. Principles of Data Mining and Knowledge Discovery, pages 71-82, 2002.
9. T. Calders and B. Goethals. Mining all non-derivable frequent itemsets. In Proc. PKDD Int. Conf. Principles of Data Mining and Knowledge Discovery, pages 74-85. Springer, 2002.
10. A. Evfimievski, R. Srikant, R. Agrawal, and J. Gehrke. Privacy preserving mining of association rules. In Proc. KDD Int. Conf. Knowledge Discovery in Databases, 2002.
11. S. Hettich and S. D. Bay. The UCI KDD Archive, [http://kdd.ics.uci.edu]. Irvine, CA: University of California, Department of Information and Computer Science, 1999.
12. M. Kryszkiewicz. Concise representation of frequent patterns based on disjunction-free generators. In Proc. IEEE Int. Conf. on Data Mining, pages 305-312, 2001.
13. M. Kryszkiewicz. Upper bound on the length of generalized disjunction free patterns. In SSDBM, 2004.
14. M. Kryszkiewicz and M. Gajek. Concise representation of frequent patterns based on generalized disjunction-free generators. In Proc. PaKDD Pacific-Asia Conf. on Knowledge Discovery and Data Mining, pages 159-171, 2002.
15. M. Kryszkiewicz and M. Gajek. Why to apply generalized disjunction-free generators representation of frequent patterns? In Proc. International Syposium on Methodologies for Intelligent Systems, pages 382-392, 2002.
16. H. Mannila and H. Toivonen. Multiple uses of frequent sets and condensed representations. In Proc. KDD Int. Conf. Knowledge Discovery in Databases, 1996.
17. N. Pasquier, Y. Bastide, R. Taouil, and L. Lakhal. Discovering frequent closed itemsets for association rules. In Proc. ICDT Int. Conf. Database Theory, pages 398-416, 1999.
18. J. Van den Bussche F. Geerts, B. Goethals. A tight upper bound on the number of candidate patterns. In Proc. ICDM, pages 155-162, 2001.