Modeling KDD processes within the inductive database framework

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

Volumn 1676, Issue , 1999, Pages 293-302

  Boulicaut, Jean François ^a   Klemettinen, Mika ^b   Mannila, Heikki ^c  

^a UNIVERSITÉ DE LYON   (France)

^b UNIVERSITY OF HELSINKI   (Finland)

^c MICROSOFT RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATA WAREHOUSES; QUERY LANGUAGES; QUERY PROCESSING; WAREHOUSES;

CLOSURE PROPERTY; DATA MANIPULATIONS; DATA MINING PROCESS; FUNCTIONAL DEPENDENCY; INDUCED PROPERTIES; INDUCTIVE DATABASE; USEFUL PROPERTIES; VERY LARGE DATABASE;

DATA MINING;

EID: 84903198782     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-48298-9_31     Document Type: Conference Paper

References (18)

1. R. Agrawal, T. Imielinski, and A. Swami. Mining association rules between sets of items in large databases. In SIGMOD’93, pages 207-216, May 1993. ACM.
2. R. Agrawal, H. Mannila, R. Srikant, H. Toivonen, and A. I. Verkamo. Fast discovery of association rules. In Advances in Knowledge Discovery and Data Mining, pages 307-328. AAAI Press, 1996.
3. J.-F. Boulicaut. A KDD framework to support database audit. In WITS’98, volume TR 19, pages 257-266, December 1998. University of Jyväskylä.
4. J.-F. Boulicaut, M. Klemettinen, and H. Mannila. Querying inductive databases: A case study on the MINE RULE operator. In PKDD’98, volume 1510 of LNAI, pages 194-202, September 1998. Springer-Verlag.
5. S. Brin, R. Motwani, J. D. Ullman, and S. Tsur. Dynamic itemset counting and implication rules for market basket data. In SIGMOD’97, pages 255-264, 1997. ACM Press.
6. L. Dehaspe and L. De Raedt. Mining association rules in multiple relations. In Proceedings 7th Int’l Workshop on Inductive Logic Programming, volume 1297 of LNAI, pages 125-132. Springer-Verlag, 1997.
7. J. M. Hellerstein. Optimization techniques for queries with expensive methods. ACM Transaction on Database Systems, 1998. Available at http://www.cs.berkeley.edu/_jmh/miscpapers/todsxfunc.ps.
8. T. Imielinski and H. Mannila. A database perspective on knowledge discovery. Communications of the ACM, 39(11):58-64, November 1996.
9. T. Imielinski, A. Virmani, and A. Abdulghani. DataMine: Application programming interface and query language for database mining. In KDD’96, pages 256-261, August 1996. AAAI Press.
10. M. Klemettinen, H. Mannila, and H. Toivonen. Rule discovery in telecommunication alarm data. Journal of Network and Systems Management, 1999. To appear.
11. H. Mannila. Inductive databases and condensed representations for data mining. In Proceedings of the International Logic Programming Symposium (ILPS’97), pages 21-30, October 1997. MIT Press.
12. H. Mannila. Methods and problems in data mining. In ICDT’97, volume 1186 of LNCS, pages 41-55. Springer-Verlag, 1997.
13. R. Meo, G. Psaila, and S. Ceri. A new SQL-like operator for mining association rules. In VLDB’96, pages 122-133, September 1996. Morgan Kaufmann.
14. R. Ng, L. Lakshmanan, J. Han, and A. Pang. Exploratory mining and pruning optimizations of constrained associations rules. In SIGMOD’98, pages 13-24, 1998. ACM Press.
15. P. Smyth and R. M. Goodman. An information theoretic approach to rule induction from databases. IEEE Transactions on Knowledge and Data Engineering, 4(4):301-316, August 1992.
16. R. Srikant, Q. Vu, and R. Agrawal. Mining association rules with item constraints. In KDD’97, pages 67-73, 1997. AAAI Press.
17. D. Tsur, J. D. Ullman, S. Abiteboul, C. Clifton, R. Motwani, S. Nestorov, and A. Rosenthal. Query ocks: A generalization of association-rule mining. In SIG- MOD’98, pages 1-12, 1998. ACM Press.
18. A. Tuzhilin. A pattern discovery algebra. In SIGMOD Workshop on Research Is- sues on Data Mining and Knowledge Discovery, Technical Report 97-07 University of British Columbia, pages 71-76, 1997.