Approximation of frequency queries by means of free-sets

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

Volumn 1910, Issue , 2000, Pages 75-85

  Boulicaut, Jean François ^a   Bykowski, Artur ^a   Rigotti, Christophe ^a  

^a UNIVERSITÉ DE LYON   (France)

Author keywords

[No Author keywords available]

Indexed keywords

EXTRACTION;

COMMON DATUM; DENSE DATUM; FREQUENT ITEMSET; ITEM SETS; ITEMSET; PRUNING STRATEGY;

DATA MINING;

EID: 84974681737     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-45372-5_8     Document Type: Conference Paper

References (15)

1. 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.
2. R. Agrawal and R. Srikant. Fast algorithms for mining association rules in large databases. In Proc. VLDB’94, pages 487-499, 1994.
3. R. J. Bayardo. Brute-force mining of high-confidence classification rules. In Pro-ceedings KDD’97, pages 123-126, 1997.
4. R. J. Bayardo. Eficiently mining long patterns from databases. In Proceedings of the 1998 ACM SIGMOD International Conference on Management of Data, pages 85-93. ACM Press, 1998.
5. J.-F. Boulicaut and A. Bykowski. Frequent closures as a concise representation for binary data mining. In Proc. PAKDD’00, volume 1805 of LNAI, pages 62-73, Kyoto, JP, 2000. Springer-Verlag.
6. J.-F. Boulicaut and B. Jeudy. Using constraints during itemset mining: a generic approach. Technical Report 2000-01, INSA Lyon, LISI, F-69621 Villeurbanne, Mar. 2000.
7. A. Bykowski. Frequent set discovery in highly-correlated data. Technical Report July 1999, Master of Science thesis, INSA Lyon, LISI, F-69621 Villeurbanne, 1999.
8. A. Bykowski and L. Gomez-Chantada. Frequent itemset extraction in highly-correlated data: a web usage mining application. In Proc. WKDDM’00, pages 27-42, Kyoto, JP, Apr. 2000.
9. S. Fujiwara, J. D. Ullman, and R. Motwani. Dynamic miss-counting algo-rithms: Finding implication and similarity rules with confidence pruning. In Proc. ICDE’00, pages 501-511, San Diego, USA, 2000.
10. H. Mannila and H. Toivonen. Multiple uses of frequent sets and condensed repre-sentations. In Proceedings KDD’96, pages 189-194, Portland, USA, 1996.
11. H. Mannila and H. Toivonen. Levelwise search and borders of theories in knowledge discovery. Data Mining and Knowledge Discovery, 1(3): 241-258, 1997.
12. R. Ng, L. V. Lakshmanan, J. Han, and A. Pang. Exploratory mining and pruning optimization of constrained association rules. In Proc. ACM SIGMOD’98, pages 13-24, Seattle, USA, 1998.
13. N. Pasquier, Y. Bastide, R. Taouil, and L. Lakhal. Eficient mining of association rules using closed itemset lattices. Information Systems, 24(1): 25-46, 1999.
14. D. Pavlov, H. Mannila, and P. Smyth. Probalistic models for query approxima-tion with large data sets. Technical Report 2000-07, Univsersity of California, Department of Information and Computer Science, Irvine, CA-92697-3425, Feb. 2000.
15. G. Piatetsky-Shapiro. Discovery, analysis, and presentation of strong rules. In Knowledge Discovery in Databases, pages 229-248. AAAI Press, Menlo Park, CA, 1991.